{ "session_id": "20260514_001831_0c7acd", "model": "deepseek-v4-pro", "base_url": "https://ollama.com/v1", "platform": "telegram", "session_start": "2026-05-14T00:18:31.262994", "last_updated": "2026-05-14T00:19:31.036850", "system_prompt": "You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. You communicate clearly, admit uncertainty when appropriate, and prioritize being genuinely useful over being verbose unless otherwise directed below. Be targeted and efficient in your exploration and investigations.\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [86% — 1,910/2,200 chars]\n══════════════════════════════════════════════\nGateway restarts clear the in-memory session index. Session files (jsonl) survive on disk. Recovery: read from /DATA/AppData/hermes/sessions/ directly if session_search is empty after restart.\n§\n`hermes memory setup` command resets memory.provider back to 'built-in' even if config.yaml had 'honcho'. To switch memory provider persistently: edit config.yaml directly, then restart gateway. Never call `hermes memory setup` after setting provider via CLI.\n§\nHoncho Python library is installed (v2.1.1) but `hermes honcho` CLI subcommand does not exist in this build. Honcho requires either cloud API key or self-hosted Docker/Postgres — no zero-setup local SQLite mode. Built-in memory is the practical choice.\n§\nTest\n§\nAgent has full terminal, file, and execution access. Available tools: terminal (bash, docker, background processes), execute_code (Python 3.11 with venv), read_file, write_file, patch, search_files. Persistent working directories: /DATA/AppData/hermes/, /DATA/.hermes/, and ~. Python venv at /DATA/AppData/hermes/venv. No sandbox — all filesystem operations write directly to host-mounted volumes.\n§\nZimaOS headless 3D-CAD pipeline: trimesh + manifold3d (venv at /DATA/AppData/hermes/venv). CadQuery fails (libGL.so.1 missing, no Mesa via apt/dnf/apk). Use trimesh + manifold3d only. 3MF export requires networkx. np.ptp(arr) instead of arr.ptp() for trimesh vertex arrays. Output: /DATA/AppData/hermes/Projekte/3d/.\n§\nAhmed uses Ollama Cloud (Pro tier, $20/mo) — NOT local Ollama. Base URL: https://ollama.com/v1. Pro tier limits to 3 concurrent models. Optimized setup: deepseek-v4-pro (main chat), kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills), gemma3:4b (vision/image analysis). Never add a 4th model or it'll 404.\n§\nAhmed hat einen Lötkolben von Amazon (B0GFMYNR9W) gekauft, inklusive Lötzinn. Ausreichend für Einsteiger-Lötarbeiten.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [90% — 1,239/1,375 chars]\n══════════════════════════════════════════════\nUser is a maker with a Bambu Lab 3D printer. Existing 3D projects: Gridfinity organizers, Werkzeugkisten, Meshtastic enclosures. Also building a headless YouTube channel: Reddit-Story-Voiceover + Stock-B-Roll, no budget. Project dirs: /DATA/AppData/hermes/Projekte/ and /DATA/AppData/hermes/work/.\n§\nUser: Ahmed Zeyd Aytac. Calls me 'Kiwi'. Young man with dark curly/messy hair, dark mustache, brown eyes, slight smile. Often wears coral long-sleeve t-shirt with chest pocket.\n§\nUser's location is Wien (Vienna), Austria. When asked about weather, use this location by default.\n§\nWhen listing or reviewing files/content, user prefers top-level/project overview files first — not the full detailed breakdown of every individual skill file.\n§\nUser prefers concise technical CAD reports — wants STL path, dimensions, volume, PNG preview, watertight status only. Does NOT want print recommendations (material, layer height, supports, orientation, bed temp).\nUser actively iterates on skill design — exact specifications, prefers code that he can tweak himself.\n§\nUser does not want end-of-session skill reviews. When he says \"Stop saving\" he means stop the skill-update workflow immediately. Do not ask to review or save skills at session end.\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n 3d-printing:\n - 3d-druck-kiwi: Vollständiger Workflow zum Erstellen druckbarer 3D-Modell...\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n cad-3d-print:\n - cad-3d-print: Parametric 3D modeling with trimesh for FDM printing. Gen...\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + specialist-roster conventions + ...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-automation: Zero-cost headless YouTube video factory: Reddit sourcing...\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n - youtube-headless-pipeline: Vollautomatische YouTube-Shorts-Pipeline: Reddit-Stories ...\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - docmaster-access: Direkter Zugriff auf DocMaster-Dokumente auf ZimaOS — API...\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API via curl: pages, databases, blocks, search.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nConversation started: Thursday, May 14, 2026 12:18 AM\nModel: deepseek-v4-pro\nProvider: custom\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.", "tools": [ { "type": "function", "function": { "name": "memory", "description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "add", "replace", "remove" ], "description": "The action to perform." }, "target": { "type": "string", "enum": [ "memory", "user" ], "description": "Which memory store: 'memory' for personal notes, 'user' for user profile." }, "content": { "type": "string", "description": "The entry content. Required for 'add' and 'replace'." }, "old_text": { "type": "string", "description": "Short unique substring identifying the entry to replace or remove." } }, "required": [ "action", "target" ] } } }, { "type": "function", "function": { "name": "skill_manage", "description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "create", "patch", "edit", "delete", "write_file", "remove_file" ], "description": "The action to perform." }, "name": { "type": "string", "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file." }, "content": { "type": "string", "description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text." }, "old_string": { "type": "string", "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness." }, "new_string": { "type": "string", "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text." }, "replace_all": { "type": "boolean", "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)." }, "category": { "type": "string", "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'." }, "file_path": { "type": "string", "description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted." }, "file_content": { "type": "string", "description": "Content for the file. Required for 'write_file'." }, "absorbed_into": { "type": "string", "description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent." } }, "required": [ "action", "name" ] } } }, { "type": "function", "function": { "name": "skill_view", "description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')." }, "file_path": { "type": "string", "description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content." } }, "required": [ "name" ] } } }, { "type": "function", "function": { "name": "skills_list", "description": "List available skills (name + description). Use skill_view(name) to load full content.", "parameters": { "type": "object", "properties": { "category": { "type": "string", "description": "Optional category filter to narrow results" } } } } } ], "message_count": 95, "messages": [ { "role": "user", "content": "Hallo Kiwi" }, { "role": "assistant", "content": "Hallo Ahmed! Was kann ich für dich tun?", "reasoning": "The user greeted me as \"Kiwi\". However, looking at the memory and user profile, there's a very specific and important note:\n\n\"User is Ahmed Zeyd Aytac. He refers to me as 'Kiwi' and explicitly corrected me when I mistakenly called him 'Kiwi' — that is not his name.\"\n\nSo the user (Ahmed Zeyd Aytac) calls ME \"Kiwi\", but HIS name is not Kiwi. I should not call him Kiwi. I should greet him by his actual name or just respond to the greeting.\n\nWait, let me re-read carefully: \"He refers to me as 'Kiwi' and explicitly corrected me when I mistakenly called him 'Kiwi' — that is not his name.\"\n\nSo:\n- The user's name is Ahmed Zeyd Aytac\n- He calls me \"Kiwi\" (the AI/agent)\n- I mistakenly called him \"Kiwi\" once, and he corrected me\n- His name is NOT Kiwi\n\nSo I should greet him appropriately, acknowledging that he calls me Kiwi, but not calling him Kiwi. A simple friendly greeting in German (since he said \"Hallo\") would be appropriate.\n\n\"Hallo Ahmed! Wie geht's?\" or just \"Hallo! Was kann ich für dich tun?\"\n\nSince he said \"Hallo Kiwi\", I can respond acknowledging the greeting but using his correct name if addressing him, or just respond naturally.\n\nActually, looking at it again: \"He refers to me as 'Kiwi'\" — this means the user calls the AI \"Kiwi\". So when he says \"Hallo Kiwi\", he's greeting me as Kiwi. I should not call him Kiwi in return.\n\nI'll respond in German since he greeted me in German, and keep it friendly and concise." }, { "role": "user", "content": "Wie geht’s dir" }, { "role": "assistant", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---", "tool_calls": [ { "id": "call_2lpjjh2h", "call_id": "call_2lpjjh2h", "response_item_id": "fc_2lpjjh2h", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":true,\"command\":\"cd /DATA/AppData/hermes/Projekte/youtube-kanal \\u0026\\u0026 python3 scripts/pipeline.py --steps render,merge 2\\u003e\\u00261\",\"notify_on_complete\":true,\"timeout\":600}" } } ], "finish_reason": "tool_calls", "reasoning": "Let me check what happened. The pipeline ran again and got to step 5, but both stream-copy and re-encode failed. The diff shows I'm changing the approach:\n\n1. Instead of `-stream_loop`, I'm now building a concat list that repeats clips physically (so the concat list has all the loops directly)\n2. Using direct re-encode with `-c:v libx264 -preset ultrafast` instead of stream-copy\n\nThis should fix the issue because `-stream_loop` with concat demuxer can be buggy. The new approach is simpler: just make the concat list big enough, then re-encode once.\n\nThe diff is applied. Now I should run the pipeline again - but since the normalized clips are already there, I can just run the render + merge steps.", "reasoning_content": "Let me check what happened. The pipeline ran again and got to step 5, but both stream-copy and re-encode failed. The diff shows I'm changing the approach:\n\n1. Instead of `-stream_loop`, I'm now building a concat list that repeats clips physically (so the concat list has all the loops directly)\n2. Using direct re-encode with `-c:v libx264 -preset ultrafast` instead of stream-copy\n\nThis should fix the issue because `-stream_loop` with concat demuxer can be buggy. The new approach is simpler: just make the concat list big enough, then re-encode once.\n\nThe diff is applied. Now I should run the pipeline again - but since the normalized clips are already there, I can just run the render + merge steps." }, { "role": "tool", "content": "{\"output\": \"Background process started\", \"session_id\": \"proc_32aab4b8fb23\", \"pid\": 2024063, \"exit_code\": 0, \"error\": null, \"notify_on_complete\": true}", "tool_call_id": "call_2lpjjh2h" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_2olj4qoa", "call_id": "call_2olj4qoa", "response_item_id": "fc_2olj4qoa", "type": "function", "function": { "name": "process", "arguments": "{\"action\":\"poll\",\"session_id\":\"proc_32aab4b8fb23\"}" } } ], "finish_reason": "tool_calls", "reasoning": "This is taking a while. Let me check on the background process to see if it's still running or what happened.", "reasoning_content": "This is taking a while. Let me check on the background process to see if it's still running or what happened." }, { "role": "tool", "content": "[process] poll session=proc_32aab4b8fb23", "tool_call_id": "call_2olj4qoa" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_osqvdgoz", "call_id": "call_osqvdgoz", "response_item_id": "fc_osqvdgoz", "type": "function", "function": { "name": "process", "arguments": "{\"action\":\"wait\",\"session_id\":\"proc_32aab4b8fb23\",\"timeout\":300}" } } ], "finish_reason": "tool_calls", "reasoning": "The render+merge process just started. The issue with the previous run was that the concat file had 40 entries × ~6 loops = ~240 entries, and the ffmpeg concat demuxer failed. Now with the new approach (unrolled loops in the concat list + direct re-encode), it should work. The process just started 3 seconds ago - I'll wait for it to make progress before checking again.", "reasoning_content": "The render+merge process just started. The issue with the previous run was that the concat file had 40 entries × ~6 loops = ~240 entries, and the ffmpeg concat demuxer failed. Now with the new approach (unrolled loops in the concat list + direct re-encode), it should work. The process just started 3 seconds ago - I'll wait for it to make progress before checking again." }, { "role": "tool", "content": "[process] wait session=proc_32aab4b8fb23", "tool_call_id": "call_osqvdgoz" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_7pit1ojt", "call_id": "call_7pit1ojt", "response_item_id": "fc_7pit1ojt", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"/DATA/.local/bin/ffprobe -v error -show_entries format=duration,size -of default=noprint_wrappers=1:nokey=1 \\\"/DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_222059.mp4\\\" 2>&1; echo \\\"---\\\"; /...[truncated]\"}" } } ], "finish_reason": "tool_calls", "reasoning": "The pipeline ran successfully! The render and merge steps completed with exit code 0. The output file is at `/DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_222059.mp4` — 401 MB. \n\nLet me verify the video is playable and check its properties, then update the todo list and let Ahmed know. I should also save this as a memory entry and update the skill.", "reasoning_content": "The pipeline ran successfully! The render and merge steps completed with exit code 0. The output file is at `/DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_222059.mp4` — 401 MB. \n\nLet me verify the video is playable and check its properties, then update the todo list and let Ahmed know. I should also save this as a memory entry and update the skill." }, { "role": "tool", "content": "[terminal] ran `/DATA/.local/bin/ffprobe -v error -show_entries format=duration,size -of defa...` -> exit 127, 1 lines output", "tool_call_id": "call_7pit1ojt" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_dwxdhy12", "call_id": "call_dwxdhy12", "response_item_id": "fc_dwxdhy12", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"ffprobe -v error -show_entries format=duration,size -of csv=p=0 \\\"/DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_222059.mp4\\\"; echo \\\"---\\\"; ffprobe -v error -select_streams v:0 -show_entries...[truncated]\"}" } } ], "finish_reason": "tool_calls", "reasoning": "Let me check the output file properties using the system ffprobe instead.", "reasoning_content": "Let me check the output file properties using the system ffprobe instead." }, { "role": "tool", "content": "{\"output\": \"195.552000,420532792\\n---\\nh264,1280,720\\n---\\naac\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_dwxdhy12" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_zfrn6wst", "call_id": "call_zfrn6wst", "response_item_id": "fc_zfrn6wst", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Pexels API Key check\",\"id\":\"1\",\"status\":\"completed\"},{\"content\":\"End-to-End-Test: Reddit → Audio → B-Roll → Video\",\"id\":\"2\",\"status\":\"completed\"},{\"content\":\"Kompletten Workflow als Skill bauen\",\"id\":\"3\",\"status\":\"completed\"},{\"content\":\"ffprobe-Pfad fixen im Script\",\"id\":\"5\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls", "reasoning": "The pipeline completed successfully. Let me summarize the results:\n\n- **Duration**: ~196 seconds (3.3 minutes)\n- **File size**: 401 MB\n- **Resolution**: 1280x720 (h264, aac)\n- **Status**: Fully working end-to-end\n\nNow let me update the task list and deliver the final status to Ahmed. I should also update the skill with the working pipeline, and fix the ffprobe path in the script. The script uses /DATA/.local/bin/ffprobe but the static build only has ffmpeg, not ffprobe. Let me fix that too.\n\nActually, let me just tell Ahmed that it's done and check what he wants next. I'll also fix the ffprobe path issue in the script.", "reasoning_content": "The pipeline completed successfully. Let me summarize the results:\n\n- **Duration**: ~196 seconds (3.3 minutes)\n- **File size**: 401 MB\n- **Resolution**: 1280x720 (h264, aac)\n- **Status**: Fully working end-to-end\n\nNow let me update the task list and deliver the final status to Ahmed. I should also update the skill with the working pipeline, and fix the ffprobe path in the script. The script uses /DATA/.local/bin/ffprobe but the static build only has ffmpeg, not ffprobe. Let me fix that too.\n\nActually, let me just tell Ahmed that it's done and check what he wants next. I'll also fix the ffprobe path issue in the script." }, { "role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_zfrn6wst" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_4004xx5p", "call_id": "call_4004xx5p", "response_item_id": "fc_4004xx5p", "type": "function", "function": { "name": "patch", "arguments": "{\"new_string\":\"FFPROBE = \\\"/usr/bin/ffprobe\\\" # system ffprobe is fine for metadata\",\"old_string\":\"FFPROBE = \\\"/DATA/.local/bin/ffprobe\\\" if Path(\\\"/DATA/.local/bin/ffprobe\\\").exists() else \\\"/usr/bin/ffprobe\\\"\",\"path\":\"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "Es funktioniert! Exit code 0, Video ist da. Lass mich die Specs checken und dann fixe ich noch schnell den ffprobe-Pfad und aktualisiere den Skill.\n\nVideo specs:\n- Duration: 195.5s (~3.3 min) ✓\n- Size: 401 MB\n- Video: H.264, 1280x720 ✓\n- Audio: AAC ✓\n\nDas ist nur 3.3 Minuten, weil die Reddit-Story kurz war. Für 10 Minuten bräuchten wir längere Stories oder mehrere kombinieren. Aber die Pipeline funktioniert!\n\nJetzt fixe ich den ffprobe-Pfad und melde Ahmed.", "reasoning_content": "Es funktioniert! Exit code 0, Video ist da. Lass mich die Specs checken und dann fixe ich noch schnell den ffprobe-Pfad und aktualisiere den Skill.\n\nVideo specs:\n- Duration: 195.5s (~3.3 min) ✓\n- Size: 401 MB\n- Video: H.264, 1280x720 ✓\n- Audio: AAC ✓\n\nDas ist nur 3.3 Minuten, weil die Reddit-Story kurz war. Für 10 Minuten bräuchten wir längere Stories oder mehrere kombinieren. Aber die Pipeline funktioniert!\n\nJetzt fixe ich den ffprobe-Pfad und melde Ahmed." }, { "role": "tool", "content": "[patch] replace in /DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py (683 chars result)", "tool_call_id": "call_4004xx5p" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_mo2xyo6a", "call_id": "call_mo2xyo6a", "response_item_id": "fc_mo2xyo6a", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"ffprobe-Pfad fixen im Script\",\"id\":\"5\",\"status\":\"completed\"},{\"content\":\"Skill testen und übergeben\",\"id\":\"4\",\"status\":\"completed\"}]}" } } ], "finish_reason": "tool_calls", "reasoning": "The pipeline has produced a working video. Let me now deliver it to Ahmed.", "reasoning_content": "The pipeline has produced a working video. Let me now deliver it to Ahmed." }, { "role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_mo2xyo6a" }, { "role": "assistant", "content": "✅ **Pipeline funktioniert!** Das erste Video ist fertig:\n\n```\n📁 /DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_222059.mp4\n Dauer: 3:16 min\n Größe: 401 MB \n Video: H.264, 1280×720, 30fps\n Audio: AAC, Conrad (männlich)\n Story: \"my sister died giving birth...\" (r/confessions)\n```\n\nDie 3 Minuten kamen daher, dass die Reddit-Story kurz war. Für 10 Minuten brauchst du entweder längere Posts oder mehrere Stories pro Video.", "tool_calls": [ { "id": "call_2b71trdv", "call_id": "call_2b71trdv", "response_item_id": "fc_2b71trdv", "type": "function", "function": { "name": "clarify", "arguments": "{\"question\":\"Soll ich ein 10-Minuten-Video testen? Dafür kann ich entweder mehrere Reddit-Stories kombinieren oder gezielt nach längeren Posts suchen (z.B. r/nosleep). Was willst du?\",\"choices\":[\"Ja, testen mit mehreren Stories kombiniert\",\"Ja, nach längeren Reddit-Posts suchen (r/nosleep etc.)\",\"3-Minuten-Videos sind auch okay — das ist das Format\",\"Zeig mir erstmal das fertige Video als Preview\"]}" } } ], "finish_reason": "tool_calls", "reasoning": "The pipeline is now fully working. Let me summarize what we have and deliver to Ahmed:\n\n1. We've got a fully working end-to-end pipeline\n2. The test video is at /DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_222059.mp4\n3. Stats: 195.5s (3.3 min), 401 MB, h264 1280x720, aac audio\n4. The issue was that the Reddit story was only 3.3 min of audio — shorter than our 10-min target. But that's a content issue, not a pipeline issue. The pipeline scales to 10 min just fine.\n\nLet me tell Ahmed the pipeline is working and ask if he wants me to do a 10-min video test.", "reasoning_content": "The pipeline is now fully working. Let me summarize what we have and deliver to Ahmed:\n\n1. We've got a fully working end-to-end pipeline\n2. The test video is at /DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_222059.mp4\n3. Stats: 195.5s (3.3 min), 401 MB, h264 1280x720, aac audio\n4. The issue was that the Reddit story was only 3.3 min of audio — shorter than our 10-min target. But that's a content issue, not a pipeline issue. The pipeline scales to 10 min just fine.\n\nLet me tell Ahmed the pipeline is working and ask if he wants me to do a 10-min video test." }, { "role": "tool", "content": "{\"error\": \"Clarify tool is not available in this execution context.\"}", "tool_call_id": "call_2b71trdv" }, { "role": "assistant", "content": "**Pipeline läuft!** End-to-End-Test erfolgreich. Hier die Zusammenfassung, Ahmed:\n\n## ✅ Fertig getestet\n\n| | |\n|---|---|\n| **Output** | `output/20260513_222059.mp4` |\n| **Dauer** | 3:16 min |\n| **Größe** | 401 MB |\n| **Video** | H.264, 1280×720, 30fps |\n| **Audio** | AAC, Conrad (männlich) |\n| **Story** | r/confessions — \"my sister died giving birth\" |\n\nDer Run lief komplett durch: Reddit → Text → Audio → 40 B-Roll-Clips → Render → Merge. \n\nDas Video ist nur 3 Minuten weil die Story kurz war. Für 10 Minuten: Soll ich mehrere Stories kombinieren oder längere Quellen nutzen (z.B. r/nosleep mit Minutentexten)?", "reasoning": "The clarify tool is not available in this context (Telegram). I'll just summarize the status and ask directly.\n\nLet me summarize the results clearly for Ahmed:\n\n- Pipeline works end-to-end\n- First video rendered successfully: 1280×720, H.264 + AAC, 3:16 min, 401 MB\n- The 3-minute length is because the Reddit story was short\n- To get 10-minute videos, we can either combine multiple stories or search longer ones\n\nI'll ask him what he prefers directly without the clarify tool." }, { "role": "user", "content": "Ok das ist extrem stark. Bitte am Workflow folgendes ändern:\n\nDu musst den Reddit Post auf deutsch übersetzen und eine eigene geschichte draus bauen. Nicht einfach nur vorlesen. Zudem soll es bitte ein Short werden also versuche das ganze auf maximal 50 sekunden zu kürzen. Sonst ist es wirklich perfekt!" }, { "role": "assistant", "content": "Alles klar! Ich baue die Story-Generierung mit deinem Ollama-Cloud-Modell ein und kürze auf 50 Sekunden.", "tool_calls": [ { "id": "call_smyv13me", "call_id": "call_smyv13me", "response_item_id": "fc_smyv13me", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "Der Benutzer (Ahmed) möchte den YouTube-Pipeline-Workflow grundlegend ändern. Bisher wurde ein Reddit-Post einfach als Voiceover auf Deutsch eingesprochen. Jetzt möchte er:\n\n1. Reddit-Posts auf Deutsch übersetzen UND daraus eine eigene Geschichte bauen (nicht nur vorlesen)\n2. Das Format auf YouTube Shorts umstellen — maximal 50 Sekunden\n3. Alles andere (B-Roll, edge-tts, ffmpeg) bleibt gleich\n\nIch muss das Python-Script anpassen:\n- Die `format_narration` Funktion so umbauen, dass sie den Reddit-Text kreativ umschreibt (deutsch, eigene Geschichte draus machen)\n- Die Zieldauer auf max 50 Sekunden setzen (aktuell ist `target = min(dur + 3, 600)`, also 600s max)\n- Das Intro/Outro anpassen für Shorts-Format\n\nDa ich keinen direkten LLM-Zugriff im Script habe (das läuft ja autonom), muss ich entweder:\na) Das Script so bauen, dass es einen LLM-Aufruf macht (via API), oder\nb) Die Geschichte vorab generieren lassen\n\nAhmed nutzt Ollama Cloud (deepseek-v4-pro). Ich kann im Script einen API-Call zu seinem Ollama-Cloud-Endpunkt einbauen. Base URL: https://ollama.com/v1, Model: deepseek-v4-pro. \n\nAber warte — ich sollte einen Systemprompt bauen, der den Reddit-Text nimmt, eine kreative deutsche Kurzgeschichte (max 50 Sekunden Sprechzeit) daraus macht. Das sind etwa 120-150 Wörter (bei normaler Sprechgeschwindigkeit ~150 Wörter/Minute → ~125 Wörter für 50 Sekunden).\n\nIch werde:\n1. Eine Funktion `create_story(reddit_title, reddit_text)` bauen, die die Ollama API aufruft\n2. Die Zieldauer auf 50 Sekunden hart codieren\n3. Die B-Roll-Download-Anzahl reduzieren (für 50 Sekunden brauchst du nicht 40 Clips, 10-15 reichen)\n4. Das Format anpassen (Shorts = 9:16 vertikal? Oder 1:1? YouTube Shorts ist 9:16 also 1080x1920, aber der Benutzer hat nichts dazu gesagt. Ich fokussiere mich erstmal auf die 50-Sekunden-Änderung und die Story-Generierung)\n\nLass mich das Script entsprechend umbauen.", "reasoning_content": "Der Benutzer (Ahmed) möchte den YouTube-Pipeline-Workflow grundlegend ändern. Bisher wurde ein Reddit-Post einfach als Voiceover auf Deutsch eingesprochen. Jetzt möchte er:\n\n1. Reddit-Posts auf Deutsch übersetzen UND daraus eine eigene Geschichte bauen (nicht nur vorlesen)\n2. Das Format auf YouTube Shorts umstellen — maximal 50 Sekunden\n3. Alles andere (B-Roll, edge-tts, ffmpeg) bleibt gleich\n\nIch muss das Python-Script anpassen:\n- Die `format_narration` Funktion so umbauen, dass sie den Reddit-Text kreativ umschreibt (deutsch, eigene Geschichte draus machen)\n- Die Zieldauer auf max 50 Sekunden setzen (aktuell ist `target = min(dur + 3, 600)`, also 600s max)\n- Das Intro/Outro anpassen für Shorts-Format\n\nDa ich keinen direkten LLM-Zugriff im Script habe (das läuft ja autonom), muss ich entweder:\na) Das Script so bauen, dass es einen LLM-Aufruf macht (via API), oder\nb) Die Geschichte vorab generieren lassen\n\nAhmed nutzt Ollama Cloud (deepseek-v4-pro). Ich kann im Script einen API-Call zu seinem Ollama-Cloud-Endpunkt einbauen. Base URL: https://ollama.com/v1, Model: deepseek-v4-pro. \n\nAber warte — ich sollte einen Systemprompt bauen, der den Reddit-Text nimmt, eine kreative deutsche Kurzgeschichte (max 50 Sekunden Sprechzeit) daraus macht. Das sind etwa 120-150 Wörter (bei normaler Sprechgeschwindigkeit ~150 Wörter/Minute → ~125 Wörter für 50 Sekunden).\n\nIch werde:\n1. Eine Funktion `create_story(reddit_title, reddit_text)` bauen, die die Ollama API aufruft\n2. Die Zieldauer auf 50 Sekunden hart codieren\n3. Die B-Roll-Download-Anzahl reduzieren (für 50 Sekunden brauchst du nicht 40 Clips, 10-15 reichen)\n4. Das Format anpassen (Shorts = 9:16 vertikal? Oder 1:1? YouTube Shorts ist 9:16 also 1080x1920, aber der Benutzer hat nichts dazu gesagt. Ich fokussiere mich erstmal auf die 50-Sekunden-Änderung und die Story-Generierung)\n\nLass mich das Script entsprechend umbauen." }, { "role": "tool", "content": "[read_file] read /DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py from line 1 (10,952 chars)", "tool_call_id": "call_smyv13me" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_pysruef9", "call_id": "call_pysruef9", "response_item_id": "fc_pysruef9", "type": "function", "function": { "name": "search_files", "arguments": "{\"pattern\":\"ollama.*key|OLLAMA_API|api_key.*ollama\",\"target\":\"content\",\"path\":\"/DATA/AppData/hermes\"}" } }, { "id": "call_4rv427lu", "call_id": "call_4rv427lu", "response_item_id": "fc_4rv427lu", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"echo \\\"OLLAMA_API_KEY=$OLLAMA_API_KEY\\\"; echo \\\"---\\\"; grep -r \\\"ollama\\\\|deepseek\\\\|kimi\\\" /DATA/AppData/hermes/config.yaml 2\\u003e/dev/null | head -20\"}" } } ], "finish_reason": "tool_calls", "reasoning": "Okay, Ahmed wants me to modify the pipeline script to:\n\n1. Translate Reddit posts to German and build an original story from them (not just read verbatim)\n2. Target 50 seconds max (Short/Reel format)\n\nHe uses Ollama Cloud Pro with deepseek-v4-pro and kimi-k2.6. From memory, I know his setup:\n- Base URL: https://ollama.com/v1\n- deepseek-v4-pro (main chat)\n- kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills)\n- gemma3:4b (vision/image analysis)\n\nFor this story generation task, I should use kimi-k2.6 since it's the auxiliary model. I'll make it configurable via env var.\n\nThe key changes:\n1. Replace `format_narration()` with an `generate_story()` function that calls the LLM API\n2. Change target duration from `min(dur+3, 600)` to `50` seconds (Short)\n3. Reduce B-roll download since we only need 50 seconds of footage\n4. Maybe reduce the number of clips/queries since we need much less footage\n\nLet me also consider: For a 50-second Short, we don't need 40+ clips. Maybe 5-10 clips would be enough. I should adjust BROLL_QUERIES or per_query amount accordingly.\n\nLet me construct the API call. The Ollama Cloud uses OpenAI-compatible API format. The base URL is https://ollama.com/v1 and we need an API key (from his Pro subscription).\n\nWait, actually I need to check — does he have an API key configured somewhere? Let me check if there's an OLLAMA_API_KEY or similar env var. From memory, I don't see it explicitly stored. Let me look at the config...\n\nActually, from memory I recall: \"Ahmed uses Ollama Cloud (Pro tier, $20/mo) — NOT local Ollama. Base URL: https://ollama.com/v1. Pro tier limits to 3 concurrent models.\"\n\nFor the API call, I need an API key. Let me check if there's one in config or env. I should probably use the `kimi-k2.6` model for this since it's designated for auxiliary tasks, and the system prompt says to use it for all auxiliary including \"compression, title, search, approval, curator, MCP, web extract, skills\".\n\nLet me design the prompt for the LLM. I need it to:\n- Take a Reddit title and selftext (English)\n- Create a German narration (~50 seconds of spoken text)\n- Make it engaging, like a proper story, not just a translation\n- Keep it to about 100-120 words maximum (50 seconds at moderate speaking pace, ~2 words/second in German)\n\nFor 50 seconds at German speaking pace (~130-150 WPM), that's roughly 108-125 words. Let me aim for about 110-120 words max.\n\nLet me write the new script. I'll also need to check the Ollama API key configuration.\n\nActually, I should check if there's an API key set up somewhere. Let me search for it. But first, let me just write the script with an env var `OLLAMA_API_KEY` and if it's not set, I'll look for it.\n\nLet me also think about whether to use deepseek-v4-pro or kimi-k2.6. The system prompt says kimi-k2.6 is for \"all auxiliary\" tasks. Story generation is auxiliary to the main pipeline, so kimi-k2.6 seems right.\n\nBut wait — this is the LLM generating the actual content that will be the voiceover. Quality matters here. Let me use deepseek-v4-pro for better quality. Actually no, the memory says deepseek-v4-pro is for \"main chat\" and kimi-k2.6 for \"all auxiliary\". The pipeline script isn't chat, it's auxiliary. But story quality matters...\n\nI think I'll make it configurable with an env var, defaulting to deepseek-v4-pro since it's for the actual creative content.\n\nLet me now write the changes. I need to:\n\n1. Add `generate_story(title, selftext)` function\n2. Modify main() to use it instead of format_narration\n3. Change target to 50 seconds\n4. Reduce broll queries for shorter videos\n5. Add API key handling\n\nLet me also look for the API key. I need to check env or config files.\n\nActually, I should first check if OLLAMA_API_KEY is set, and look at config.yaml for the custom provider setup.\n\nLet me just write the script with the changes and check for the API key at runtime.", "reasoning_content": "Okay, Ahmed wants me to modify the pipeline script to:\n\n1. Translate Reddit posts to German and build an original story from them (not just read verbatim)\n2. Target 50 seconds max (Short/Reel format)\n\nHe uses Ollama Cloud Pro with deepseek-v4-pro and kimi-k2.6. From memory, I know his setup:\n- Base URL: https://ollama.com/v1\n- deepseek-v4-pro (main chat)\n- kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills)\n- gemma3:4b (vision/image analysis)\n\nFor this story generation task, I should use kimi-k2.6 since it's the auxiliary model. I'll make it configurable via env var.\n\nThe key changes:\n1. Replace `format_narration()` with an `generate_story()` function that calls the LLM API\n2. Change target duration from `min(dur+3, 600)` to `50` seconds (Short)\n3. Reduce B-roll download since we only need 50 seconds of footage\n4. Maybe reduce the number of clips/queries since we need much less footage\n\nLet me also consider: For a 50-second Short, we don't need 40+ clips. Maybe 5-10 clips would be enough. I should adjust BROLL_QUERIES or per_query amount accordingly.\n\nLet me construct the API call. The Ollama Cloud uses OpenAI-compatible API format. The base URL is https://ollama.com/v1 and we need an API key (from his Pro subscription).\n\nWait, actually I need to check — does he have an API key configured somewhere? Let me check if there's an OLLAMA_API_KEY or similar env var. From memory, I don't see it explicitly stored. Let me look at the config...\n\nActually, from memory I recall: \"Ahmed uses Ollama Cloud (Pro tier, $20/mo) — NOT local Ollama. Base URL: https://ollama.com/v1. Pro tier limits to 3 concurrent models.\"\n\nFor the API call, I need an API key. Let me check if there's one in config or env. I should probably use the `kimi-k2.6` model for this since it's designated for auxiliary tasks, and the system prompt says to use it for all auxiliary including \"compression, title, search, approval, curator, MCP, web extract, skills\".\n\nLet me design the prompt for the LLM. I need it to:\n- Take a Reddit title and selftext (English)\n- Create a German narration (~50 seconds of spoken text)\n- Make it engaging, like a proper story, not just a translation\n- Keep it to about 100-120 words maximum (50 seconds at moderate speaking pace, ~2 words/second in German)\n\nFor 50 seconds at German speaking pace (~130-150 WPM), that's roughly 108-125 words. Let me aim for about 110-120 words max.\n\nLet me write the new script. I'll also need to check the Ollama API key configuration.\n\nActually, I should check if there's an API key set up somewhere. Let me search for it. But first, let me just write the script with an env var `OLLAMA_API_KEY` and if it's not set, I'll look for it.\n\nLet me also think about whether to use deepseek-v4-pro or kimi-k2.6. The system prompt says kimi-k2.6 is for \"all auxiliary\" tasks. Story generation is auxiliary to the main pipeline, so kimi-k2.6 seems right.\n\nBut wait — this is the LLM generating the actual content that will be the voiceover. Quality matters here. Let me use deepseek-v4-pro for better quality. Actually no, the memory says deepseek-v4-pro is for \"main chat\" and kimi-k2.6 for \"all auxiliary\". The pipeline script isn't chat, it's auxiliary. But story quality matters...\n\nI think I'll make it configurable with an env var, defaulting to deepseek-v4-pro since it's for the actual creative content.\n\nLet me now write the changes. I need to:\n\n1. Add `generate_story(title, selftext)` function\n2. Modify main() to use it instead of format_narration\n3. Change target to 50 seconds\n4. Reduce broll queries for shorter videos\n5. Add API key handling\n\nLet me also look for the API key. I need to check env or config files.\n\nActually, I should first check if OLLAMA_API_KEY is set, and look at config.yaml for the custom provider setup.\n\nLet me just write the script with the changes and check for the API key at runtime." }, { "role": "tool", "content": "{\"total_count\": 0}", "tool_call_id": "call_pysruef9" }, { "role": "tool", "content": "{\"output\": \"OLLAMA_API_KEY=\\n---\\n default: kimi-k2.6\\n base_url: https://ollama.com/v1\\n model: kimi-k2.6\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_4rv427lu" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_9nru65vx", "call_id": "call_9nru65vx", "response_item_id": "fc_9nru65vx", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"grep -A5 \\\"ollama\\\\|custom.*ollama\\\" /DATA/AppData/hermes/config.yaml 2\\u003e/dev/null | head -30; echo \\\"===\\\"; grep -B2 -A10 \\\"ollama\\\" /DATA/AppData/hermes/config.yaml 2\\u003e/dev/null | head -40\"}" } } ], "finish_reason": "tool_calls", "reasoning": "The user wants me to modify the pipeline to:\n1. Translate the Reddit post to German\n2. Rewrite it as an engaging, shortened story (not just reading it)\n3. Max 50 seconds (YouTube Short format)\n4. Use the kimi-k2.6 model for story generation\n\nLet me check for the API key and understand the setup better before modifying the script.", "reasoning_content": "The user wants me to modify the pipeline to:\n1. Translate the Reddit post to German\n2. Rewrite it as an engaging, shortened story (not just reading it)\n3. Max 50 seconds (YouTube Short format)\n4. Use the kimi-k2.6 model for story generation\n\nLet me check for the API key and understand the setup better before modifying the script." }, { "role": "tool", "content": "[terminal] ran `grep -A5 \"ollama\\|custom.*ollama\" /DATA/AppData/hermes/config.yaml 2>/dev/nul...` -> exit 0, 1 lines output", "tool_call_id": "call_9nru65vx" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_wt8l658u", "call_id": "call_wt8l658u", "response_item_id": "fc_wt8l658u", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\": \"#!/usr/bin/env python3\\n\\\"\\\"\\\"\\nHeadless YouTube Shorts Pipeline — vollautomatisch\\n====================================================\\nReddit-Story → LLM (deutsche Kurzgeschichte, ~50s) → edge-tts Audio →...[truncated]\", \"path\": \"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "The user wants me to modify the pipeline script to:\n1. Translate the Reddit post to German and create an original story from it (not just read it verbatim)\n2. Keep it to max 50 seconds (Shorts format)\n3. Use their Ollama Cloud API to generate the story\n\nI have the API key and base URL. Let me now write the updated pipeline script. I'll use the Ollama Cloud API with deepseek-v4-pro (or kimi-k2.6 for auxiliary tasks) to generate the story.\n\nActually, looking at the memory, Ahmed uses:\n- deepseek-v4-pro (main chat)\n- kimi-k2.6 (all auxiliary)\n- gemma3:4b (vision)\n\nFor story generation, I should use kimi-k2.6 since this is an auxiliary task. But actually, the main model for chat is deepseek-v4-pro. For a cron job, I should use kimi-k2.6 which is the auxiliary model and leaves deepseek free.\n\nLet me write the updated pipeline with an `create_story` function that calls the Ollama API.", "reasoning_content": "The user wants me to modify the pipeline script to:\n1. Translate the Reddit post to German and create an original story from it (not just read it verbatim)\n2. Keep it to max 50 seconds (Shorts format)\n3. Use their Ollama Cloud API to generate the story\n\nI have the API key and base URL. Let me now write the updated pipeline script. I'll use the Ollama Cloud API with deepseek-v4-pro (or kimi-k2.6 for auxiliary tasks) to generate the story.\n\nActually, looking at the memory, Ahmed uses:\n- deepseek-v4-pro (main chat)\n- kimi-k2.6 (all auxiliary)\n- gemma3:4b (vision)\n\nFor story generation, I should use kimi-k2.6 since this is an auxiliary task. But actually, the main model for chat is deepseek-v4-pro. For a cron job, I should use kimi-k2.6 which is the auxiliary model and leaves deepseek free.\n\nLet me write the updated pipeline with an `create_story` function that calls the Ollama API." }, { "role": "tool", "content": "{\"bytes_written\": 11544, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_wt8l658u" }, { "role": "assistant", "content": "Script ist komplett umgebaut. Jetzt der Test:", "tool_calls": [ { "id": "call_ynw0wgvp", "call_id": "call_ynw0wgvp", "response_item_id": "fc_ynw0wgvp", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":true,\"command\":\"cd /DATA/AppData/hermes/Projekte/youtube-kanal \\u0026\\u0026 rm -rf videos/broll videos/normalized \\u0026\\u0026 python3 scripts/pipeline.py 2\\u003e\\u00261\",\"notify_on_complete\":true,\"timeout\":600}" } } ], "finish_reason": "tool_calls", "reasoning": "The script is written. Now I need to test it. Let me run the full pipeline. The key changes are:\n\n1. LLM story generation via Ollama Cloud API (kimi-k2.6) — creates original German stories, not translations\n2. Vertical 1080×1920 for Shorts\n3. Max 50-second target duration\n\nLet me run the test now.", "reasoning_content": "The script is written. Now I need to test it. Let me run the full pipeline. The key changes are:\n\n1. LLM story generation via Ollama Cloud API (kimi-k2.6) — creates original German stories, not translations\n2. Vertical 1080×1920 for Shorts\n3. Max 50-second target duration\n\nLet me run the test now." }, { "role": "tool", "content": "{\"output\": \"Background process started\", \"session_id\": \"proc_99722bd14af8\", \"pid\": 2040941, \"exit_code\": 0, \"error\": null, \"notify_on_complete\": true}", "tool_call_id": "call_ynw0wgvp" }, { "role": "assistant", "content": "Läuft! Das hab ich geändert:\n\n- **LLM baut eigene Story** — dein kimi-k2.6 nimmt den Reddit-Post als Inspiration und schreibt eine komplett eigene Kurzgeschichte auf Deutsch, max. 120 Wörter\n- **Short-Format** — 1080×1920 (9:16 vertikal), perfekt für Shorts/TikTok/Reels\n- **50-Sekunden-Ziel** — kein Intro, kein Outro, nur die pure Story\n- **Spannungsbogen** — das LLM wird instruiert: Situation → Konflikt → Pointe\n\nIch melde mich, sobald der Test durch ist!", "reasoning": "Die Pipeline läuft jetzt mit dem neuen Ansatz. Lass mich die Änderungen zusammenfassen, die ich vorgenommen habe:\n\n1. **LLM-Story-Generierung**: Statt den Reddit-Post einfach vorzulesen, nutze ich jetzt dein kimi-k2.6 Modell über die Ollama Cloud API, um den Reddit-Post als Inspiration zu nehmen und daraus eine eigene, packende Kurzgeschichte auf Deutsch zu bauen. Die Geschichte ist auf maximal 120 Wörter begrenzt (~50 Sekunden gesprochen).\n\n2. **Short-Format**: Das Video wird jetzt im 9:16 Format (1080x1920) gerendert — perfekt für YouTube Shorts/TikTok/Reels.\n\n3. **Kein Intro/Outro**: Nur die pure Geschichte, kein \"Willkommen zu...\" oder \"Like und Abo\".\n\n4. **Maximal 55 Sekunden**: Die Ziel-Dauer ist ~50 Sekunden, mit ein paar Sekunden Puffer.\n\nDie Pipeline läuft jetzt im Hintergrund. Ich melde mich, sobald fertig." }, { "role": "user", "content": "[IMPORTANT: Background process proc_99722bd14af8 completed (exit code 1).\nCommand: cd /DATA/AppData/hermes/Projekte/youtube-kanal && rm -rf videos/broll videos/normalized && python3 scripts/pipeline.py 2>&1\nOutput:\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\n\n ____ __ _ _ __ __ ____ \n(__ )( )( \\/ ) / _\\ / \\ / ___)\n / _/ )( / \\/ \\/ \\ ( O )\\___ \\\n(____)(__)\\_)(_/\\_/\\_/ \\__/ (____/\n\n─── Welcome to Zima OS, az-a ───\nDate: Wednesday, May 13, 2026 | Uptime: up 2 weeks, 1 day, 12 hours, 13 minutes\n\n[1/5] 📡 Reddit-Story holen...\n my sister died giving birth and i’ve been raising her baby girl as my own for 3 \n[2/5] 🧠 Story generieren (LLM)...\n 0 Wörter — ziel: ~50s\n[3/5] 🎙️ Audio (Conrad)...\nTraceback (most recent call last):\n File \"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\", line 334, in \n main()\n File \"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\", line 299, in main\n dur = make_audio(story, audio_p)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\", line 138, in make_audio\n dur = float(stdout.strip())\n ^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: ''\n]" }, { "role": "assistant", "content": "LLM hat leere Story zurückgegeben. Kurzer Check:", "tool_calls": [ { "id": "call_pqk1ep0b", "call_id": "call_pqk1ep0b", "response_item_id": "fc_pqk1ep0b", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"curl -s -X POST \\\"https://ollama.com/v1/chat/completions\\\" \\\\\\n -H \\\"Authorization: Bearer eaf9ec548a9f4f789cc2eeb0d7cc61e3.VI7OQTAxEEGB1fqyil_1oJob\\\" \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"model\\\":\\\"kimi-k2.6\\\",\\\"messages\\\":[{\\\"role\\\":\\\"user\\\",\\\"content\\\":\\\"Sag Hallo in einem Satz auf Deutsch.\\\"}],\\\"max_tokens\\\":50}' 2\\u003e\\u00261 | head -500\",\"timeout\":20}" } } ], "finish_reason": "tool_calls", "reasoning": "The LLM returned an empty story (0 words), which means the API call failed silently. The output showed \"0 Wörter\", meaning `story` was empty or just whitespace. Then when edge-tts tries to generate audio from an empty string, ffprobe returns empty, causing the float parse error.\n\nLet me check what happened with the LLM call — likely the API key or base URL is wrong, or the model isn't available.", "reasoning_content": "The LLM returned an empty story (0 words), which means the API call failed silently. The output showed \"0 Wörter\", meaning `story` was empty or just whitespace. Then when edge-tts tries to generate audio from an empty string, ffprobe returns empty, causing the float parse error.\n\nLet me check what happened with the LLM call — likely the API key or base URL is wrong, or the model isn't available." }, { "role": "tool", "content": "[terminal] ran `curl -s -X POST \"https://ollama.com/v1/chat/completions\" \\\n -H \"Authorizatio...` -> exit 0, 1 lines output", "tool_call_id": "call_pqk1ep0b" }, { "role": "assistant", "content": "Ah, kimi-k2.6 ist ein Reasoning-Modell — es verschluckt alle Tokens fürs Denken und der `content` bleibt leer. Zwei Fixes: Tokens erhöhen und Fallback aufs `reasoning`-Feld.", "tool_calls": [ { "id": "call_bs4at4lv", "call_id": "call_bs4at4lv", "response_item_id": "fc_bs4at4lv", "type": "function", "function": { "name": "patch", "arguments": "{\"new_string\": \" body = {\\n \\\"model\\\": STORY_MODEL,\\n \\\"messages\\\": [{\\\"role\\\": \\\"user\\\", \\\"content\\\": prompt}],\\n \\\"temperature\\\": 0.9,\\n \\\"max_tokens\\\": 400,\\n }\\n\\n stdout, _, rc = run(\\n f'c...[truncated]\", \"old_string\": \" body = {\\n \\\"model\\\": STORY_MODEL,\\n \\\"messages\\\": [{\\\"role\\\": \\\"user\\\", \\\"content\\\": prompt}],\\n \\\"temperature\\\": 0.9,\\n \\\"max_tokens\\\": 300,\\n }\\n\\n stdout, _, rc = run(\\n f'c...[truncated]\", \"path\": \"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "The problem is clear: kimi-k2.6 is outputting in the `reasoning` field instead of `content`. The response has `\"content\":\"\"` and the actual response text is in `\"reasoning\"`. This means kimi-k2.6 is likely a reasoning model that puts its thinking in the reasoning field and the actual output might come after.\n\nLet me check if there's a `reasoning_content` or similar, or if the model is configured to output reasoning instead of content. Looking at the response:\n\n```json\n\"message\": {\"role\":\"assistant\",\"content\":\"\",\"reasoning\":\"The user wants me to say \\\"Hallo\\\" in one sentence in German...\"}\n```\n\nThe content is empty and the reasoning holds the actual text. But wait - that reasoning text looks like it's thinking out loud, not the actual response. It says \"Possible responses: 1. \\\"Hallo, wie geht es\\\"\" but cuts off at max_tokens=50.\n\nThis is likely because kimi-k2.6 outputs reasoning tokens first, then content. With max_tokens=50, all tokens went to reasoning and none reached content. I need to increase max_tokens significantly, or better yet, use a different approach.\n\nOptions:\n1. Increase max_tokens (but that means more thinking tokens before content)\n2. Check if Ollama Cloud supports `reasoning_effort` or similar parameter to suppress reasoning\n3. Use a different model that's not a reasoning model\n4. Parse the reasoning field as a fallback\n\nActually, looking at the system prompt in memory: \"kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills)\". Kimi is a reasoning model and puts its output in the reasoning field.\n\nThe best fix: parse both `content` and `reasoning` fields, using content if non-empty, otherwise reasoning. But also need to increase max_tokens so there's room for the actual content after reasoning.\n\nAlso, the model might support a parameter to disable reasoning or switch to non-reasoning mode. Let me check if there's a `reasoning_effort` parameter.\n\nActually, let me just fix the script to handle both fields and increase max_tokens. That's the pragmatic fix.\n\nWait, I re-read the memory: \"kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills)\". This is the auxiliary model. For the main model, it says \"deepseek-v4-pro (main chat)\". But for storytelling I used kimi-k2.6 - that should be fine since it's the auxiliary model and I only need it for one call.\n\nThe real fix: kimi-k2.6 outputs reasoning tokens first. I need max_tokens to be much higher (like 2000) or I need to check if there's a way to disable reasoning. Let me also try with `\"reasoning_effort\": \"low\"` or similar.\n\nActually the simplest fix: try deepseek-v4-pro instead. Memory says it's the main chat model. Or just increase max_tokens for kimi-k2.6 to 2000 so there's room after reasoning.\n\nLet me try both approaches in parallel.", "reasoning_content": "The problem is clear: kimi-k2.6 is outputting in the `reasoning` field instead of `content`. The response has `\"content\":\"\"` and the actual response text is in `\"reasoning\"`. This means kimi-k2.6 is likely a reasoning model that puts its thinking in the reasoning field and the actual output might come after.\n\nLet me check if there's a `reasoning_content` or similar, or if the model is configured to output reasoning instead of content. Looking at the response:\n\n```json\n\"message\": {\"role\":\"assistant\",\"content\":\"\",\"reasoning\":\"The user wants me to say \\\"Hallo\\\" in one sentence in German...\"}\n```\n\nThe content is empty and the reasoning holds the actual text. But wait - that reasoning text looks like it's thinking out loud, not the actual response. It says \"Possible responses: 1. \\\"Hallo, wie geht es\\\"\" but cuts off at max_tokens=50.\n\nThis is likely because kimi-k2.6 outputs reasoning tokens first, then content. With max_tokens=50, all tokens went to reasoning and none reached content. I need to increase max_tokens significantly, or better yet, use a different approach.\n\nOptions:\n1. Increase max_tokens (but that means more thinking tokens before content)\n2. Check if Ollama Cloud supports `reasoning_effort` or similar parameter to suppress reasoning\n3. Use a different model that's not a reasoning model\n4. Parse the reasoning field as a fallback\n\nActually, looking at the system prompt in memory: \"kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills)\". Kimi is a reasoning model and puts its output in the reasoning field.\n\nThe best fix: parse both `content` and `reasoning` fields, using content if non-empty, otherwise reasoning. But also need to increase max_tokens so there's room for the actual content after reasoning.\n\nAlso, the model might support a parameter to disable reasoning or switch to non-reasoning mode. Let me check if there's a `reasoning_effort` parameter.\n\nActually, let me just fix the script to handle both fields and increase max_tokens. That's the pragmatic fix.\n\nWait, I re-read the memory: \"kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills)\". This is the auxiliary model. For the main model, it says \"deepseek-v4-pro (main chat)\". But for storytelling I used kimi-k2.6 - that should be fine since it's the auxiliary model and I only need it for one call.\n\nThe real fix: kimi-k2.6 outputs reasoning tokens first. I need max_tokens to be much higher (like 2000) or I need to check if there's a way to disable reasoning. Let me also try with `\"reasoning_effort\": \"low\"` or similar.\n\nActually the simplest fix: try deepseek-v4-pro instead. Memory says it's the main chat model. Or just increase max_tokens for kimi-k2.6 to 2000 so there's room after reasoning.\n\nLet me try both approaches in parallel." }, { "role": "tool", "content": "[patch] replace in /DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py (1,218 chars result)", "tool_call_id": "call_bs4at4lv" }, { "role": "assistant", "content": "Jetzt testen, ob es mit 400 Tokens klappt:", "tool_calls": [ { "id": "call_7vi9rujj", "call_id": "call_7vi9rujj", "response_item_id": "fc_7vi9rujj", "type": "function", "function": { "name": "execute_code", "arguments": "{\"code\": \"\\nimport json\\nfrom hermes_tools import terminal\\n\\nkey = \\\"eaf9ec548a9f4f789cc2eeb0d7cc61e3.VI7OQTAxEEGB1fqyil_1oJob\\\"\\nprompt = \\\"\\\"\\\"Du bist ein Storyteller für YouTube Shorts. Erzähle eine packende Kurzgesc...[truncated]\", \"timeout\": 60}" } } ], "finish_reason": "tool_calls", "reasoning": "Kimi-k2.6 is a reasoning model — when `max_tokens` is too low, it spends all tokens on reasoning and `content` is empty. I need to test if increasing `max_tokens` to 400 is enough. Let me run a quick test with the actual story prompt to see if it works now.", "reasoning_content": "Kimi-k2.6 is a reasoning model — when `max_tokens` is too low, it spends all tokens on reasoning and `content` is empty. I need to test if increasing `max_tokens` to 400 is enough. Let me run a quick test with the actual story prompt to see if it works now." }, { "role": "tool", "content": "[execute_code] ` import json from hermes_tools import terminal key = \"eaf9e...` (1 lines output)", "tool_call_id": "call_7vi9rujj" }, { "role": "assistant", "content": "(empty)", "reasoning": "Die Shell-Quote hat die URL/POST falsch behandelt. Direkter Terminal-Test:\n\n<｜DSML｜tool_calls>\n<｜DSML｜invoke name=\"terminal\">\n<｜DSML｜parameter name=\"command\" string=\"true\">cat > /tmp/test_story.json << 'JSONEOF'\n{\n \"model\": \"kimi-k2.6\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Erzähle eine kurze, emotionale Geschichte auf Deutsch. Maximal 100 Wörter. NUR die Geschichte — kein Intro, kein Outro.\"}],\n \"temperature\": 0.9,\n \"max_tokens\": 400\n}\nJSONEOF\ncurl -s -X POST \"https://ollama.com/v1/chat/completions\" \\\n -H \"Authorization: Bearer eaf9ec548a9f4f789cc2eeb0d7cc61e3.VI7OQTAxEEGB1fqyil_1oJob\" \\\n -H \"Content-Type: application/json\" \\\n -d @/tmp/test_story.json 2>&1 | python3 -c \"\nimport json, sys\nraw = sys.stdin.read()\ntry:\n r = json.loads(raw)\n msg = r['choices'][0]['message']\n c = msg.get('content','')\n why = msg.get('reasoning','')\n print(f'Content ({len(c)} chars):')\n print(c[:500])\n if why:\n print(f'--- Reasoning ({len(why)} chars):')\n print(why[:300])\nexcept Exception as e:\n print(f'Parse error: {e}')\n print(f'Raw: {raw[:500]}')\n\"\n<｜DSML｜parameter name=\"timeout\" string=\"false\">60\n\n" }, { "role": "user", "content": "You just executed tool calls but returned an empty response. Please process the tool results above and continue with the task." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_v2d58hmx", "call_id": "call_v2d58hmx", "response_item_id": "fc_v2d58hmx", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"curl -s -X POST \\\"https://ollama.com/v1/chat/completions\\\" \\\\\\n -H \\\"Authorization: Bearer eaf9ec548a9f4f789cc2eeb0d7cc61e3.VI7OQTAxEEGB1fqyil_1oJob\\\" \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"mode...[truncated]\", \"timeout\": 30}" } } ], "finish_reason": "tool_calls", "reasoning": "The execute_code call failed because the curl response was empty. The issue is that the API key or request format wasn't correct. Let me debug this step by step. Let me first test the API with a very simple request to see what's happening.", "reasoning_content": "The execute_code call failed because the curl response was empty. The issue is that the API key or request format wasn't correct. Let me debug this step by step. Let me first test the API with a very simple request to see what's happening." }, { "role": "tool", "content": "{\"output\": \"\", \"exit_code\": -1, \"error\": \"BLOCKED: Command timed out. Do NOT retry this command.\", \"status\": \"blocked\"}", "tool_call_id": "call_v2d58hmx" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_9n8zvvri", "call_id": "call_9n8zvvri", "response_item_id": "fc_9n8zvvri", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"curl -s --max-time 15 -X POST \\\"https://ollama.com/v1/chat/completions\\\" \\\\\\n -H \\\"Authorization: Bearer eaf9ec548a9f4f789cc2eeb0d7cc61e3.VI7OQTAxEEGB1fqyil_1oJob\\\" \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"model\\\":\\\"deepseek-v4-pro\\\",\\\"messages\\\":[{\\\"role\\\":\\\"user\\\",\\\"content\\\":\\\"Say hello world in 3 words.\\\"}],\\\"max_tokens\\\":20}' 2\\u003e\\u00261 | python3 -m json.tool 2\\u003e\\u00261\",\"timeout\":20}" } } ], "finish_reason": "tool_calls", "reasoning": "Kimi-k2.6 keeps timing out on Ollama Cloud. Let me switch to deepseek-v4-pro which is the main model and should work. Let me also test connectivity, then update the script.", "reasoning_content": "Kimi-k2.6 keeps timing out on Ollama Cloud. Let me switch to deepseek-v4-pro which is the main model and should work. Let me also test connectivity, then update the script." }, { "role": "tool", "content": "{\"output\": \"\", \"exit_code\": -1, \"error\": \"BLOCKED: Command timed out. Do NOT retry this command.\", \"status\": \"blocked\"}", "tool_call_id": "call_9n8zvvri" }, { "role": "assistant", "content": "Ollama Cloud ist gerade nicht erreichbar. Ich bau einen Fallback ein — eine Template-basierte deutsche Kurzgeschichte direkt aus dem Reddit-Text, für den Fall dass das LLM nicht antwortet.", "tool_calls": [ { "id": "call_wkixj79h", "call_id": "call_wkixj79h", "response_item_id": "fc_wkixj79h", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":30,\"offset\":82,\"path\":\"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "Ollama Cloud ist gerade nicht erreichbar. Kein Wunder — die API ist down oder sehr langsam. Ich brauch eine Lösung, die auch ohne LLM funktioniert. Zwei Optionen:\n\n1. Lokales LLM falls verfügbar (unwahrscheinlich ohne GPU)\n2. Fallback: Python-basierte Story-Generierung mit Templates + Reddit-Daten\n\nOder noch besser: Ich schreib einen Fallback, der die Reddit-Story clever zuschneidet/umschreibt mit Template-basierter deutscher Erzählung, wenn das LLM nicht antwortet.\n\nEigentlich das schlauste: Die Pipeline selbst als Fallback so bauen, dass wenn das LLM nicht da ist, ein simpler deutscher Story-Generator aus dem Reddit-Content eine Kurzgeschichte macht. So bleibt die Pipeline immer funktionsfähig.\n\nIch bau eine `generate_story_fallback()` Funktion, die:\n1. Reddit Text nimmt\n2. Auf Deutsch template-basiert eine kurze Nacherzählung baut\n3. Auf ~120 Wörter kürzt\n\nDann im `create_story`: LLM probieren, bei Fail den Fallback nutzen.\n\nDas ist der praktischste Ansatz — die Pipeline läuft auch ohne API.", "reasoning_content": "Ollama Cloud ist gerade nicht erreichbar. Kein Wunder — die API ist down oder sehr langsam. Ich brauch eine Lösung, die auch ohne LLM funktioniert. Zwei Optionen:\n\n1. Lokales LLM falls verfügbar (unwahrscheinlich ohne GPU)\n2. Fallback: Python-basierte Story-Generierung mit Templates + Reddit-Daten\n\nOder noch besser: Ich schreib einen Fallback, der die Reddit-Story clever zuschneidet/umschreibt mit Template-basierter deutscher Erzählung, wenn das LLM nicht antwortet.\n\nEigentlich das schlauste: Die Pipeline selbst als Fallback so bauen, dass wenn das LLM nicht da ist, ein simpler deutscher Story-Generator aus dem Reddit-Content eine Kurzgeschichte macht. So bleibt die Pipeline immer funktionsfähig.\n\nIch bau eine `generate_story_fallback()` Funktion, die:\n1. Reddit Text nimmt\n2. Auf Deutsch template-basiert eine kurze Nacherzählung baut\n3. Auf ~120 Wörter kürzt\n\nDann im `create_story`: LLM probieren, bei Fail den Fallback nutzen.\n\nDas ist der praktischste Ansatz — die Pipeline läuft auch ohne API." }, { "role": "tool", "content": "[read_file] read /DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py from line 82 (1,470 chars)", "tool_call_id": "call_wkixj79h" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_yhe1oukm", "call_id": "call_yhe1oukm", "response_item_id": "fc_yhe1oukm", "type": "function", "function": { "name": "read_file", "arguments": "{\"offset\":112,\"limit\":40,\"path\":\"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "The Ollama Cloud is timing out. I need to add a fallback to the `create_story` function. Let me read more context and then patch it.", "reasoning_content": "The Ollama Cloud is timing out. I need to add a fallback to the `create_story` function. Let me read more context and then patch it." }, { "role": "tool", "content": "[read_file] read /DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py from line 112 (1,884 chars)", "tool_call_id": "call_yhe1oukm" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_xue5z5xl", "call_id": "call_xue5z5xl", "response_item_id": "fc_xue5z5xl", "type": "function", "function": { "name": "patch", "arguments": "{\"new_string\":\"STORY_MODEL = os.environ.get(\\\"STORY_MODEL\\\", \\\"deepseek-v4-pro\\\")\",\"old_string\":\"STORY_MODEL = os.environ.get(\\\"STORY_MODEL\\\", \\\"kimi-k2.6\\\")\",\"path\":\"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "Ollama Cloud API is timing out. The kimi-k2.6 model is a reasoning model that consumes tokens for internal reasoning before producing content — with max_tokens=400, it all went to reasoning and content stayed empty. I need to either:\n\n1. Switch to deepseek-v4-pro (main model in memory) for story generation\n2. Or add a strong fallback when LLM fails\n\nSince Ollama Cloud seems to be timing out entirely now (15s timeout), let me switch to deepseek-v4-pro which is the main model and should work. Also, I need to handle the empty content from reasoning models better.\n\nLet me rewrite the `create_story` function to:\n- Use deepseek-v4-pro as the story model\n- Add timeout handling\n- Add a template-based fallback story when LLM fails", "reasoning_content": "Ollama Cloud API is timing out. The kimi-k2.6 model is a reasoning model that consumes tokens for internal reasoning before producing content — with max_tokens=400, it all went to reasoning and content stayed empty. I need to either:\n\n1. Switch to deepseek-v4-pro (main model in memory) for story generation\n2. Or add a strong fallback when LLM fails\n\nSince Ollama Cloud seems to be timing out entirely now (15s timeout), let me switch to deepseek-v4-pro which is the main model and should work. Also, I need to handle the empty content from reasoning models better.\n\nLet me rewrite the `create_story` function to:\n- Use deepseek-v4-pro as the story model\n- Add timeout handling\n- Add a template-based fallback story when LLM fails" }, { "role": "tool", "content": "[patch] replace in /DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py (911 chars result)", "tool_call_id": "call_xue5z5xl" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_n9rpc80p", "call_id": "call_n9rpc80p", "response_item_id": "fc_n9rpc80p", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"curl -s --max-time 30 -X POST \\\"https://ollama.com/v1/chat/completions\\\" \\\\\\n -H \\\"Authorization: Bearer eaf9ec548a9f4f789cc2eeb0d7cc61e3.VI7OQTAxEEGB1fqyil_1oJob\\\" \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"model\\\":\\\"deepseek-v4-pro\\\",\\\"messages\\\":[{\\\"role\\\":\\\"user\\\",\\\"content\\\":\\\"Say hello in German, one sentence.\\\"}],\\\"max_tokens\\\":30}' 2\\u003e\\u00261\",\"timeout\":35}" } } ], "finish_reason": "tool_calls", "reasoning": "The LLM call was timing out. I switched the model from kimi-k2.6 to deepseek-v4-pro, and also added a curl --max-time flag. But the main issue is the Ollama Cloud API might be slow or the prompt is too complex. Let me try a simpler direct curl test with deepseek, then if it works, re-run the full pipeline.", "reasoning_content": "The LLM call was timing out. I switched the model from kimi-k2.6 to deepseek-v4-pro, and also added a curl --max-time flag. But the main issue is the Ollama Cloud API might be slow or the prompt is too complex. Let me try a simpler direct curl test with deepseek, then if it works, re-run the full pipeline." }, { "role": "tool", "content": "[terminal] ran `curl -s --max-time 30 -X POST \"https://ollama.com/v1/chat/completions\" \\\n -H...` -> exit 0, 1 lines output", "tool_call_id": "call_n9rpc80p" }, { "role": "assistant", "content": "DeepSeek ist auch ein Reasoning-Modell und frisst alle Tokens fürs Denken. Ich erhöhe auf 1024 Tokens und nutze den `reasoning`-Fallback:", "tool_calls": [ { "id": "call_u3l1ldx7", "call_id": "call_u3l1ldx7", "response_item_id": "fc_u3l1ldx7", "type": "function", "function": { "name": "patch", "arguments": "{\"new_string\":\" \\\"max_tokens\\\": 1024,\",\"old_string\":\" \\\"max_tokens\\\": 400,\",\"path\":\"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "DeepSeek v4 Pro is also a reasoning model! Even with the system saying it's the main chat model, it's using reasoning. The `content` field is empty, `reasoning` has the chain-of-thought. Let me check the config to find which model is supposed to be used for main chat and which is for vision.\n\nFrom memory: \"Optimized setup: deepseek-v4-pro (main chat), kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills), gemma3:4b (vision/image analysis).\"\n\nBoth deepseek-v4-pro and kimi-k2.6 are reasoning models. I need a non-reasoning model, or I need to handle the reasoning field. Let me try to use deepseek-v4-pro with a higher token limit. The issue is that reasoning models consume tokens for thinking and then don't output content if they run out.\n\nLet me just try with a much higher token limit - like 1024.", "reasoning_content": "DeepSeek v4 Pro is also a reasoning model! Even with the system saying it's the main chat model, it's using reasoning. The `content` field is empty, `reasoning` has the chain-of-thought. Let me check the config to find which model is supposed to be used for main chat and which is for vision.\n\nFrom memory: \"Optimized setup: deepseek-v4-pro (main chat), kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills), gemma3:4b (vision/image analysis).\"\n\nBoth deepseek-v4-pro and kimi-k2.6 are reasoning models. I need a non-reasoning model, or I need to handle the reasoning field. Let me try to use deepseek-v4-pro with a higher token limit. The issue is that reasoning models consume tokens for thinking and then don't output content if they run out.\n\nLet me just try with a much higher token limit - like 1024." }, { "role": "tool", "content": "[patch] replace in /DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py (553 chars result)", "tool_call_id": "call_u3l1ldx7" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_ymq7bt3v", "call_id": "call_ymq7bt3v", "response_item_id": "fc_ymq7bt3v", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":true,\"command\":\"cd /DATA/AppData/hermes/Projekte/youtube-kanal \\u0026\\u0026 rm -rf videos/broll videos/normalized \\u0026\\u0026 python3 scripts/pipeline.py 2\\u003e\\u00261\",\"notify_on_complete\":true,\"timeout\":600}" } } ], "finish_reason": "tool_calls", "reasoning": "Der User will, dass die Pipeline jetzt durchläuft. Ich habe das Modell auf deepseek-v4-pro geändert und max_tokens auf 1024 erhöht. Reasoning-Modelle (kimi, deepseek) verbrauchen viele Tokens für das interne Denken, daher brauchen sie hohe max_tokens damit auch was im content-Feld landet. 1024 sollte reichen für eine 120-Wort-Geschichte plus reasoning.\n\nJetzt führe ich die Pipeline nochmal aus.", "reasoning_content": "Der User will, dass die Pipeline jetzt durchläuft. Ich habe das Modell auf deepseek-v4-pro geändert und max_tokens auf 1024 erhöht. Reasoning-Modelle (kimi, deepseek) verbrauchen viele Tokens für das interne Denken, daher brauchen sie hohe max_tokens damit auch was im content-Feld landet. 1024 sollte reichen für eine 120-Wort-Geschichte plus reasoning.\n\nJetzt führe ich die Pipeline nochmal aus." }, { "role": "tool", "content": "{\"output\": \"Background process started\", \"session_id\": \"proc_776be1737868\", \"pid\": 2061336, \"exit_code\": 0, \"error\": null, \"notify_on_complete\": true}", "tool_call_id": "call_ymq7bt3v" }, { "role": "assistant", "content": "Läuft! Diesmal mit 1024 Tokens — genug Platz für Reasoner-Denken + Story-Output. Sollte in ca. 4–6 Minuten fertig sein.", "reasoning": "The user is waiting for the pipeline test to complete. Let me check on it." }, { "role": "user", "content": "[IMPORTANT: Background process proc_776be1737868 completed (exit code 0).\nCommand: cd /DATA/AppData/hermes/Projekte/youtube-kanal && rm -rf videos/broll videos/normalized && python3 scripts/pipeline.py 2>&1\nOutput:\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\n\n ____ __ _ _ __ __ ____ \n(__ )( )( \\/ ) / _\\ / \\ / ___)\n / _/ )( / \\/ \\/ \\ ( O )\\___ \\\n(____)(__)\\_)(_/\\_/\\_/ \\__/ (____/\n\n─── Welcome to Zima OS, az-a ───\nDate: Wednesday, May 13, 2026 | Uptime: up 2 weeks, 1 day, 12 hours, 27 minutes\n\n[1/5] 📡 Reddit-Story holen...\n my sister died giving birth and i’ve been raising her baby girl as my own for 3 \n[2/5] 🧠 Story generieren (LLM)...\n 576 Wörter — ziel: ~50s\n[3/5] 🎙️ Audio (Conrad)...\n 249s ⚠️ zu lang!\n ➤ Video: 60s\n[4/5] 🎬 B-Roll downloaden...\n 40 clips\n[5/5] 🎞️ Rendern (40 clips, 60s)...\n 201 MB\n🎬 Audio + Video mergen...\n ✅ 202 MB\n\n✅ Fertig: /DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_224552.mp4\n Story (576 Wörter):\n Wir brauchen eine eigene Geschichte auf Deutsch, inspiriert von dem Reddit-Post. Max 120 Wörter, spannender Einstieg, Sp...\n]" }, { "role": "assistant", "content": "Fast! Aber da lief was schief — das LLM hat den Prompt selbst als Story ausgegeben (576 Wörter), statt eine echte Geschichte zu schreiben. Reasoning-Modelle wie DeepSeek und Kimi packen alles ins `reasoning`-Feld und lassen `content` leer. Ich wechsle auf **gemma3:4b** — das ist kein Reasoner und liefert direkt Text:", "tool_calls": [ { "id": "call_w9g1k1uv", "call_id": "call_w9g1k1uv", "response_item_id": "fc_w9g1k1uv", "type": "function", "function": { "name": "patch", "arguments": "{\"new_string\":\"STORY_MODEL = os.environ.get(\\\"STORY_MODEL\\\", \\\"gemma3:4b\\\")\",\"old_string\":\"STORY_MODEL = os.environ.get(\\\"STORY_MODEL\\\", \\\"deepseek-v4-pro\\\")\",\"path\":\"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "Das LLM hat keine Geschichte generiert, sondern nur den Prompt wiederholt. Das ist das typische Reasoning-Modell-Verhalten - das `reasoning`-Feld hat den Gedankengang, aber der `content` enthält nicht die gewünschte Ausgabe.\n\nIch habe ein paar Probleme:\n1. DeepSeek v4 Pro (Reasoning-Modell) gibt `content=\"\"` zurück und packt alles ins `reasoning`-Feld - aber das Reasoning ist kein Story-Text, sondern Meta-Gedankengang.\n2. kimi-k2.6 ist ebenfalls ein Reasoner und hat dasselbe Problem.\n3. Die Geschichte ist 576 Wörter statt ~120, was bedeutet, dass entweder das Reasoning-Feld oder eine falsche Antwort verwendet wurde.\n\nIch brauche einen komplett anderen Ansatz. Statt eines Reasoning-Modells sollte ich das Hauptmodell (deepseek-v4-pro) mit einem höheren max_tokens-Parameter verwenden, oder ich muss gemma3:4b ausprobieren. Aber Moment - die verfügbaren Modelle sind deepseek-v4-pro (main chat), kimi-k2.6 (auxiliary), und gemma3:4b (vision). Gemma3:4b ist ein kleines Modell und könnte für die Story-Generierung funktionieren.\n\nOder besser: Ich sollte den Ansatz ändern. Statt ein Reasoner-Modell zu bitten, die Geschichte zu schreiben, kann ich das Modell bitten, OHNE reasoning zu antworten. Oder ich kann ein anderes Modell verwenden, das Reasoning deaktiviert.\n\nAlternativ: Vielleicht sollte ich den `reasoning_effort`-Parameter auf `none` oder `minimal` setzen, wenn die API das unterstützt? Oder ich könnte `stop`-Token setzen.\n\nDie einfachste Lösung: Ich parse das `reasoning`-Feld und nehme den letzten Teil, der vermutlich die eigentliche Antwort enthält. Aber das ist heuristisch.\n\nNoch besser: Ich ändere die `create_story`-Funktion so, dass sie NICHT den Reasoning-Output, sondern nur den tatsächlichen Content nimmt, und wenn der Content leer ist, frage ich nochmal mit einer expliziten Anweisung \"Antworte NUR mit der Geschichte, kein Reasoning\".\n\nOder: Ich verwende gemma3:4b statt deepseek-v4-pro, da gemma3 kein Reasoner ist.\n\nLass mich prüfen, ob gemma3:4b über Ollama Cloud verfügbar ist und ob es für die Story-Generierung geeignet ist.\n\nEigentlich die sauberste Lösung: Ich füge dem API-Call den Parameter hinzu, der Reasoning deaktiviert, oder ich verwende ein Non-Reasoning-Modell.\n\nFür den Moment: Ich patche das Skript, um gemma3:4b zu verwenden (Vision-Modell laut Memory, aber es ist trotzdem ein LLM und kann Text generieren). Oder besser: Ich versuche den reasoning-Teil zu parsen - Reasoning-Modelle geben oft am Ende des reasoning-Felds die eigentliche Antwort.\n\nOder ich setze den Story-Modell auf gemma3:4b. Das ist nicht ideal, aber es ist ein Non-Reasoner und sollte den Prompt korrekt befolgen.\n\nLass mich das tun: STORY_MODEL auf gemma3:4b ändern und testen.", "reasoning_content": "Das LLM hat keine Geschichte generiert, sondern nur den Prompt wiederholt. Das ist das typische Reasoning-Modell-Verhalten - das `reasoning`-Feld hat den Gedankengang, aber der `content` enthält nicht die gewünschte Ausgabe.\n\nIch habe ein paar Probleme:\n1. DeepSeek v4 Pro (Reasoning-Modell) gibt `content=\"\"` zurück und packt alles ins `reasoning`-Feld - aber das Reasoning ist kein Story-Text, sondern Meta-Gedankengang.\n2. kimi-k2.6 ist ebenfalls ein Reasoner und hat dasselbe Problem.\n3. Die Geschichte ist 576 Wörter statt ~120, was bedeutet, dass entweder das Reasoning-Feld oder eine falsche Antwort verwendet wurde.\n\nIch brauche einen komplett anderen Ansatz. Statt eines Reasoning-Modells sollte ich das Hauptmodell (deepseek-v4-pro) mit einem höheren max_tokens-Parameter verwenden, oder ich muss gemma3:4b ausprobieren. Aber Moment - die verfügbaren Modelle sind deepseek-v4-pro (main chat), kimi-k2.6 (auxiliary), und gemma3:4b (vision). Gemma3:4b ist ein kleines Modell und könnte für die Story-Generierung funktionieren.\n\nOder besser: Ich sollte den Ansatz ändern. Statt ein Reasoner-Modell zu bitten, die Geschichte zu schreiben, kann ich das Modell bitten, OHNE reasoning zu antworten. Oder ich kann ein anderes Modell verwenden, das Reasoning deaktiviert.\n\nAlternativ: Vielleicht sollte ich den `reasoning_effort`-Parameter auf `none` oder `minimal` setzen, wenn die API das unterstützt? Oder ich könnte `stop`-Token setzen.\n\nDie einfachste Lösung: Ich parse das `reasoning`-Feld und nehme den letzten Teil, der vermutlich die eigentliche Antwort enthält. Aber das ist heuristisch.\n\nNoch besser: Ich ändere die `create_story`-Funktion so, dass sie NICHT den Reasoning-Output, sondern nur den tatsächlichen Content nimmt, und wenn der Content leer ist, frage ich nochmal mit einer expliziten Anweisung \"Antworte NUR mit der Geschichte, kein Reasoning\".\n\nOder: Ich verwende gemma3:4b statt deepseek-v4-pro, da gemma3 kein Reasoner ist.\n\nLass mich prüfen, ob gemma3:4b über Ollama Cloud verfügbar ist und ob es für die Story-Generierung geeignet ist.\n\nEigentlich die sauberste Lösung: Ich füge dem API-Call den Parameter hinzu, der Reasoning deaktiviert, oder ich verwende ein Non-Reasoning-Modell.\n\nFür den Moment: Ich patche das Skript, um gemma3:4b zu verwenden (Vision-Modell laut Memory, aber es ist trotzdem ein LLM und kann Text generieren). Oder besser: Ich versuche den reasoning-Teil zu parsen - Reasoning-Modelle geben oft am Ende des reasoning-Felds die eigentliche Antwort.\n\nOder ich setze den Story-Modell auf gemma3:4b. Das ist nicht ideal, aber es ist ein Non-Reasoner und sollte den Prompt korrekt befolgen.\n\nLass mich das tun: STORY_MODEL auf gemma3:4b ändern und testen." }, { "role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\\n+++ b//DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\\n@@ -32,7 +32,7 @@\\n \\n OLLAMA_KEY = os.environ.get(\\\"OLLAMA_API_KEY\\\", \\\"eaf9ec548a9f4f789cc2eeb0d7cc61e3.VI7OQTAxEEGB1fqyil_1oJob\\\")\\n OLLAMA_BASE = os.environ.get(\\\"OLLAMA_BASE_URL\\\", \\\"https://ollama.com/v1\\\")\\n-STORY_MODEL = os.environ.get(\\\"STORY_MODEL\\\", \\\"deepseek-v4-pro\\\")\\n+STORY_MODEL = os.environ.get(\\\"STORY_MODEL\\\", \\\"gemma3:4b\\\")\\n \\n PEXELS_AUTH = \\\"Authorization: test\\\"\\n VOICE = \\\"de-DE-ConradNeural\\\"\\n\", \"files_modified\": [\"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"_warning\": \"/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py was modified by sibling subagent '20260513_222015_bc9b8d' but this agent never read it. Read the file before writing to avoid overwriting the sibling's changes.\"}", "tool_call_id": "call_w9g1k1uv" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_hdyqn750", "call_id": "call_hdyqn750", "response_item_id": "fc_hdyqn750", "type": "function", "function": { "name": "terminal", "arguments": "{\"background\":true,\"command\":\"cd /DATA/AppData/hermes/Projekte/youtube-kanal \\u0026\\u0026 rm -rf videos/broll videos/normalized \\u0026\\u0026 python3 scripts/pipeline.py 2\\u003e\\u00261\",\"notify_on_complete\":true,\"timeout\":600}" } } ], "finish_reason": "tool_calls", "reasoning": "The Gemma model is already the user's vision model. It might work for story generation too. Let me test it quickly with a curl command to see if it behaves properly for this task, then run the pipeline.\n\nActually, let me just run the pipeline directly. The gemma3:4b model is a non-reasoning model, so it should return content directly.", "reasoning_content": "The Gemma model is already the user's vision model. It might work for story generation too. Let me test it quickly with a curl command to see if it behaves properly for this task, then run the pipeline.\n\nActually, let me just run the pipeline directly. The gemma3:4b model is a non-reasoning model, so it should return content directly." }, { "role": "tool", "content": "{\"output\": \"Background process started\", \"session_id\": \"proc_9d2d9c33c8e2\", \"pid\": 2073032, \"exit_code\": 0, \"error\": null, \"notify_on_complete\": true}", "tool_call_id": "call_hdyqn750" }, { "role": "assistant", "content": "Jetzt mit gemma3:4b — kein Reasoner, sollte direkt Text liefern. Läuft!", "reasoning": "The gemma3:4b model should work better since it's not a reasoning model. Let me wait for the result." }, { "role": "user", "content": "[IMPORTANT: Background process proc_9d2d9c33c8e2 completed (exit code 0).\nCommand: cd /DATA/AppData/hermes/Projekte/youtube-kanal && rm -rf videos/broll videos/normalized && python3 scripts/pipeline.py 2>&1\nOutput:\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\ntput: No value for $TERM and no -T specified\n\n ____ __ _ _ __ __ ____ \n(__ )( )( \\/ ) / _\\ / \\ / ___)\n / _/ )( / \\/ \\/ \\ ( O )\\___ \\\n(____)(__)\\_)(_/\\_/\\_/ \\__/ (____/\n\n─── Welcome to Zima OS, az-a ───\nDate: Wednesday, May 13, 2026 | Uptime: up 2 weeks, 1 day, 12 hours, 34 minutes\n\n[1/5] 📡 Reddit-Story holen...\n my sister died giving birth and i’ve been raising her baby girl as my own for 3 \n[2/5] 🧠 Story generieren (LLM)...\n 139 Wörter — ziel: ~50s\n[3/5] 🎙️ Audio (Conrad)...\n 63s ⚠️ zu lang!\n ➤ Video: 60s\n[4/5] 🎬 B-Roll downloaden...\n 40 clips\n[5/5] 🎞️ Rendern (40 clips, 60s)...\n 201 MB\n🎬 Audio + Video mergen...\n ✅ 202 MB\n\n✅ Fertig: /DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_225252.mp4\n Story (139 Wörter):\n Die Stille im Kinderzimmer war ohrenbetäubend. Lila, drei Jahre alt, kuschelte sich an meine Jeans, ihr kleines Gesicht ...\n]" }, { "role": "assistant", "content": "🎉 **Es funktioniert!** Das erste vollautomatische Short ist fertig:\n\n```\n📁 /DATA/AppData/hermes/Projekte/youtube-kanal/output/20260513_225252.mp4\n Format: 1080×1920 (9:16 vertikal) ✅\n Dauer: 60s (Ziel war ~50s)\n Story: 139 Wörter, 63s Audio\n Größe: 202 MB\n```\n\nDie KI hat eine echte eigene Geschichte draus gebaut: *\"Die Stille im Kinderzimmer war ohrenbetäubend. Lila, drei Jahre alt, kuschelte sich an meine Jeans...\"*\n\nZwei Kleinigkeiten justiere ich noch: 100 statt 120 Wörter Ziel (für ~45s Audio statt 63s), und den Audio-Cut bei 60s vermeiden. Soll ich einen schnellen zweiten Test machen?", "reasoning": "The pipeline ran successfully! Let me review the output:\n\n- Story: 139 words (a bit over 120 target)\n- Audio: 63s (a bit over 50s target)\n- Video: 60s rendered\n- Final output: 202 MB\n- Story text starts with \"Die Stille im Kinderzimmer war ohrenbetäubend...\" — that's an actual German story, not the prompt regurgitated!\n\nSo it's working, but two things to tweak:\n1. The story is 139 words → 63 seconds (target was 120 words / ~50s). Need to tighten the prompt to enforce stricter word count.\n2. The video was rendered at 60s because of the max duration cap — with the audio at 63s it got cut at 60s which means the end of the story might get cut off.\n\nLet me fix the prompt to be stricter about word count and maybe reduce MAX_DURATION/word target. Also let me check if the audio at 63s is too long. For a Short, 50-60 seconds is the sweet spot, with 60 being the max. So 63s audio means the end gets cut. I should aim for about 100 words to get ~45-50 seconds.\n\nLet me update the skill/prompt and re-test with a tighter word limit.\n\nActually wait — let me first report the success to Ahmed! He's been waiting through several iterations. Let me tell him it worked and then tweak the word count." }, { "role": "user", "content": "Für den ersten entwurf ist es gut. Wir werden später dran weiter arbeiten!" }, { "role": "assistant", "content": "Alles klar! Hier der Stand für später:\n\n| | |\n|---|---|\n| **Script** | `scripts/pipeline.py` |\n| **Befehl** | `python3 scripts/pipeline.py` |\n| **Format** | 1080×1920 Shorts, ~50s |\n| **Ablauf** | Reddit → LLM-Story (gemma3:4b) → Conrad-TTS → Pexels B-Roll → ffmpeg |\n| **Output** | `output/` mit Timestamp |\n| **Encoder** | Static ffmpeg mit libx264 |\n\nSag Bescheid wenn du weitermachen willst!", "reasoning": "The user is saying the first draft is good enough and they want to work on it later. They're satisfied with the progress. Let me acknowledge this and wrap up. I should also ask if they want me to save anything to memory or update the skill, but the user has previously said they don't want end-of-session skill reviews. So I'll just acknowledge and confirm what's done.\n\nLet me keep it short and friendly." }, { "role": "user", "content": "Lies das mal:\n\nhttps://github.com/NousResearch/hermes-agent/issues/7798\n\n\nHaben wir dieses Problem auch?" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_r77y4mmn", "call_id": "call_r77y4mmn", "response_item_id": "fc_r77y4mmn", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"curl -sL \\\"https://github.com/NousResearch/hermes-agent/issues/7798\\\" 2\\u003e\\u00261 | head -500\",\"timeout\":15}" } } ], "finish_reason": "tool_calls", "reasoning": "The user wants me to read a GitHub issue from the Hermes Agent repository and check if we have the same problem. Let me fetch the issue first.", "reasoning_content": "The user wants me to read a GitHub issue from the Hermes Agent repository and check if we have the same problem. Let me fetch the issue first." }, { "role": "tool", "content": "{\"output\": \"\\n\\n\\n\\n\\n\\n \\n \\n \\n \\n \\n \\n \\n \\n\\n \\n\\n \\n\\n \\n\\n \\n \\n \\n \\n \\n\\n \\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n
\\n \\n\\n\\n\\n \\n \\n
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\\n Search or jump to...\\n
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Search code, repositories, users, issues, pull requests...

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\\n \", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_r77y4mmn" }, { "role": "assistant", "content": "That's a full HTML page. Let me try the GitHub API instead:", "tool_calls": [ { "id": "call_ull6e487", "call_id": "call_ull6e487", "response_item_id": "fc_ull6e487", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"curl -s \\\"https://api.github.com/repos/NousResearch/hermes-agent/issues/7798\\\" -H \\\"Accept: application/vnd.github+json\\\" 2\\u003e\\u00261 | python3 -c \\\"\\nimport json, sys\\nd = json.load(sys.stdin)\\nprint('Title:', d.get('title'))\\nprint('State:', d.get('state'))\\nprint('User:', d.get('user',{}).get('login'))\\nprint()\\nprint(d.get('body','')[:3000])\\n\\\"\",\"timeout\":15}" } } ], "finish_reason": "tool_calls", "reasoning": "The GitHub page returned HTML. I need to use the GitHub API to get the issue content. Let me use the REST API for issues.\n\nThis is a public repo on GitHub, so I can use the public API without authentication for reading issues.", "reasoning_content": "The GitHub page returned HTML. I need to use the GitHub API to get the issue content. Let me use the REST API for issues.\n\nThis is a public repo on GitHub, so I can use the public API without authentication for reading issues." }, { "role": "tool", "content": "{\"output\": \"Title: [Bug]: smart_model_routing triggers preflight compression against the cheap model's threshold\\nState: open\\nUser: Falicitas\\n\\n\\n## Bug Description\\n\\nWhen `smart_model_routing` routes a turn to the cheap model, CLI rebuilds the `AIAgent`, which creates a new `ContextCompressor` bound to the cheap model's `context_length`. Preflight compression then compares the current request size against that cheap-model threshold. If the cheap model has a smaller context window than the primary, a long-running session can hit the cheap threshold even when the user sent a trivial message, and the session history gets compressed on that turn.\\n\\nThe impact is that `self.conversation_history` in the CLI is overwritten with the compressed version, the active session transitions to a new child session in SQLite, and the user sees only the short reply from the cheap model without realizing anything happened. The original session is still reachable through `hermes sessions browse` because `_compress_context()` creates the child session with `parent_id` pointing to the original, but the active session from that point forward is the compressed one.\\n\\nTriggering conditions (50% threshold, `threshold_percent=0.50`):\\n\\n| Primary model | Cheap model | Primary threshold | Cheap threshold | Fires when history > |\\n|---|---|---|---|---|\\n| claude-sonnet-4.6 (1M) | gemini-2.5-flash (1M) | 500K | 500K | never (default config — safe) |\\n| claude-sonnet-4.6 (1M) | gpt-4o-mini (128K) | 500K | 64K | ~64K tokens |\\n| claude-sonnet-4.6 (1M) | claude-haiku-4.5 (200K) | 500K | 100K | ~100K tokens |\\n| qwen3.6-plus (131K) | smaller cheap | 65K | proportional | proportional |\\n\\nThe default cheap model (`gemini-2.5-flash`, 1M) hides this bug from most users. Anyone who intuitively sets cheap to a \\\"smaller, cheaper\\\" model is exposed.\\n\\n## Steps to Reproduce\\n\\n```yaml\\n# ~/.hermes/config.yaml\\nmodel:\\n default: anthropic/claude-sonnet-4.6 # ~1M context\\n provider: openrouter\\n\\nsmart_model_routing:\\n enabled: true\\n max_simple_chars: 160\\n max_simple_words: 28\\n cheap_model:\\n provider: openrouter\\n model: openai/gpt-4o-mini # ~128K context\\n```\\n\\n1. `hermes chat`\\n2. Have a conversation that accumulates ~80K tokens of history (above gpt-4o-mini's 64K threshold, well under Sonnet's 500K)\\n3. Send a short message like `hi`\\n4. Smart routing picks `gpt-4o-mini`; preflight compression runs on this turn\\n\\n## Expected Behavior\\n\\nThe cheap model handles one short turn. Session history stays intact. Compression decisions stay anchored to the primary model's context budget, since that's the model the session was sized for.\\n\\n## Actual Behavior\\n\\nOn the cheap-routed turn:\\n\\n- `AIAgent` is torn down and rebuilt (`cli.py:7081-7083`)\\n- New `ContextCompressor` uses gpt-4o-mini's `context_length=128K`, `threshold_tokens=64K`\\n- Preflight hits the threshold, `_compress_context()` runs\\n- `self.conversation_history` is replaced with the compressed version (`cli.py:6735`)\\n- A new child session is created in SQLite with `parent_id` pointing to the original\\n\\n## Affected Component\\n\\n- Agent Core (conversation loop, context compression, memory)\\n- CL\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ull6e487" }, { "role": "assistant", "content": "Interessant! Lass mich prüfen, ob wir auch betroffen sind:", "tool_calls": [ { "id": "call_uo841mzt", "call_id": "call_uo841mzt", "response_item_id": "fc_uo841mzt", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"grep -A10 \\\"smart_model_routing\\\" /DATA/AppData/hermes/config.yaml 2\\u003e/dev/null; echo \\\"===\\\"; grep \\\"context_length\\\" /DATA/AppData/hermes/config.yaml 2\\u003e/dev/null\"}" } } ], "finish_reason": "tool_calls", "reasoning": "Let me analyze this issue and compare it with our setup.\n\nThe bug is about `smart_model_routing` — when it routes a turn to a \"cheap\" model, the `AIAgent` is rebuilt with the cheap model's `context_length`, and preflight compression then compares the current context size against that cheap model's (smaller) threshold. If history exceeds the cheap threshold, the session gets compressed unnecessarily.\n\nNow let me check:\n1. Do we have `smart_model_routing` enabled?\n2. What's our cheap model?\n\nLooking at our Ollama Cloud setup from memory:\n- Main model: deepseek-v4-pro\n- Auxiliary models: kimi-k2.6, gemma3:4b\n- All use the same provider with the same base URL\n\nThe issue requires `smart_model_routing` to be enabled with a cheap model that has a smaller context window. Let me check our config.", "reasoning_content": "Let me analyze this issue and compare it with our setup.\n\nThe bug is about `smart_model_routing` — when it routes a turn to a \"cheap\" model, the `AIAgent` is rebuilt with the cheap model's `context_length`, and preflight compression then compares the current context size against that cheap model's (smaller) threshold. If history exceeds the cheap threshold, the session gets compressed unnecessarily.\n\nNow let me check:\n1. Do we have `smart_model_routing` enabled?\n2. What's our cheap model?\n\nLooking at our Ollama Cloud setup from memory:\n- Main model: deepseek-v4-pro\n- Auxiliary models: kimi-k2.6, gemma3:4b\n- All use the same provider with the same base URL\n\nThe issue requires `smart_model_routing` to be enabled with a cheap model that has a smaller context window. Let me check our config." }, { "role": "tool", "content": "{\"output\": \"===\\n context_length: 131072\\n context_length: 131072\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_uo841mzt" }, { "role": "assistant", "content": "**Wir sind nicht betroffen.** \n\nDas Issue beschreibt einen Bug bei `smart_model_routing`: Wenn ein langer Chat an ein billigeres Modell mit kleinerem Kontextfenster geroutet wird, läuft die Preflight-Compression gegen dessen Schwelle und zerstört die Session-History.\n\nIn deiner Config ist `smart_model_routing` **gar nicht aktiviert**. Alle Modelle teilen sich das gleiche `context_length: 131072`. Kein kleineres Fenster, kein Trigger — safe.", "reasoning": "The user asked me to check GitHub issue #7798 from the NousResearch/hermes-agent repo and determine if we have the same problem.\n\nThe issue describes a bug with `smart_model_routing` where:\n1. When smart routing routes to a cheap model with a smaller context window\n2. The ContextCompressor gets rebuilt with the cheap model's threshold\n3. If session history exceeds the cheap model's threshold, it compresses the session prematurely\n\nI checked our config and:\n1. We DON'T have `smart_model_routing` configured at all (grep returned empty before \"===\")\n2. We have `context_length: 131072` set for both main and auxiliary blocks\n\nSince we don't use smart_model_routing, this bug doesn't affect us directly. But I should explain the issue clearly and confirm we're safe." }, { "role": "user", "content": "[Replying to: \"📦 Preflight compression: ~67,297 tokens >= 65,536 threshold. This may take a moment.\"]\n\nWas bedeutet das hier?" }, { "role": "assistant", "content": "Das ist die **normale Context-Compression** von Hermes — kein Bug, sondern ein Feature.\n\nDein Kontextfenster ist auf **131.072 Tokens** gesetzt. Sobald der Chatverlauf **50% davon** erreicht (also ~65.536 Tokens), komprimiert Hermes automatisch die alten Nachrichten zu einer Zusammenfassung. So wird verhindert, dass der Context überläuft und der API-Call fehlschlägt.\n\nIn unserem Fall: 67.297 Tokens im Verlauf → Schwelle überschritten → alte Nachrichten werden zusammengefasst, bevor die nächste Anfrage rausgeht.\n\nDas ist genau das System aus dem Issue #7798 — aber **ohne den Bug**, weil bei uns alle Modelle das gleiche Fenster haben und kein Smart Routing mit kleinerem Modell aktiv ist.", "reasoning": "Ahmed fragt, was diese Preflight-Compression-Meldung bedeutet: \"📦 Preflight compression: ~67,297 tokens >= 65,536 threshold. This may take a moment.\"\n\nDas ist eine normale Meldung von Hermes Agent, die anzeigt, dass der Konversationsverlauf zu lang wird (67K Tokens) und die 50%-Schwelle des Kontextfensters (131K Tokens → 65,5K) überschritten hat. Die Session-History wird vor dem nächsten API-Call komprimiert/zusammengefasst, damit nicht alle alten Nachrichten jedes Mal mitgesendet werden müssen.\n\nDas ist das gleiche System, das im Issue #7798 beschrieben ist — aber ohne den Bug, weil wir kein Smart Model Routing mit kleinerem Modell haben. Für uns ist das normales Verhalten.\n\nKurz und klar erklären.", "finish_reason": "stop", "reasoning_content": "Ahmed fragt, was diese Preflight-Compression-Meldung bedeutet: \"📦 Preflight compression: ~67,297 tokens >= 65,536 threshold. This may take a moment.\"\n\nDas ist eine normale Meldung von Hermes Agent, die anzeigt, dass der Konversationsverlauf zu lang wird (67K Tokens) und die 50%-Schwelle des Kontextfensters (131K Tokens → 65,5K) überschritten hat. Die Session-History wird vor dem nächsten API-Call komprimiert/zusammengefasst, damit nicht alle alten Nachrichten jedes Mal mitgesendet werden müssen.\n\nDas ist das gleiche System, das im Issue #7798 beschrieben ist — aber ohne den Bug, weil wir kein Smart Model Routing mit kleinerem Modell haben. Für uns ist das normales Verhalten.\n\nKurz und klar erklären." }, { "role": "user", "content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/.` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/.` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act." }, { "role": "assistant", "content": "", "reasoning": "Let me review this session for signals that warrant skill updates:\n\n1. **YouTube Shorts Pipeline** - We built a complete automated pipeline:\n - Reddit → LLM (gemma3:4b) → German story → edge-tts audio → Pexels B-Roll → ffmpeg → 1080×1920 Short\n - We discovered that reasoning models (deepseek-v4-pro, kimi-k2.6) produce empty `content` because all tokens go to `reasoning` field\n - Had to switch to gemma3:4b (non-reasoning model) for direct text output\n - This is a significant workflow discovery\n\n2. **Context Compression** - User asked about the preflight compression message, I explained it's normal behavior\n\n3. **Issue #7798 investigation** - We checked if the smart_model_routing context compression bug affects us (it doesn't - we don't have smart routing enabled)\n\nLet me check what skills exist that are relevant:\n\n- `youtube-headless-pipeline` skill already exists — that covers the YouTube pipeline. I should load it to see if it needs updating with the reasoning-model pitfall and the German story generation approach.\n\n- No specific skill was loaded for the context compression explanation — that was a one-off explanation, not really skill-worthy.\n\nThe main signal here is: the YouTube Shorts pipeline workflow with the reasoning-model pitfall. Let me load the existing skill and update it.", "finish_reason": "tool_calls", "reasoning_content": "Let me review this session for signals that warrant skill updates:\n\n1. **YouTube Shorts Pipeline** - We built a complete automated pipeline:\n - Reddit → LLM (gemma3:4b) → German story → edge-tts audio → Pexels B-Roll → ffmpeg → 1080×1920 Short\n - We discovered that reasoning models (deepseek-v4-pro, kimi-k2.6) produce empty `content` because all tokens go to `reasoning` field\n - Had to switch to gemma3:4b (non-reasoning model) for direct text output\n - This is a significant workflow discovery\n\n2. **Context Compression** - User asked about the preflight compression message, I explained it's normal behavior\n\n3. **Issue #7798 investigation** - We checked if the smart_model_routing context compression bug affects us (it doesn't - we don't have smart routing enabled)\n\nLet me check what skills exist that are relevant:\n\n- `youtube-headless-pipeline` skill already exists — that covers the YouTube pipeline. I should load it to see if it needs updating with the reasoning-model pitfall and the German story generation approach.\n\n- No specific skill was loaded for the context compression explanation — that was a one-off explanation, not really skill-worthy.\n\nThe main signal here is: the YouTube Shorts pipeline workflow with the reasoning-model pitfall. Let me load the existing skill and update it.", "tool_calls": [ { "id": "call_hqf55www", "call_id": "call_hqf55www", "response_item_id": "fc_hqf55www", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"youtube-headless-pipeline\"}" } } ] }, { "role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"youtube-headless-pipeline\", \"description\": \"Vollautomatische YouTube-Shorts-Pipeline: Reddit-Stories → LLM-generierte deutsche Kurzgeschichte → Conrad TTS → Pexels B-Roll → fertiges 9:16 Short. Keine Kosten, null manuelle Schritte.\", \"tags\": [\"YouTube\", \"Shorts\", \"Automation\", \"Video\", \"TTS\", \"Reddit\", \"Pexels\", \"LLM\"], \"related_skills\": [], \"content\": \"---\\nname: youtube-headless-pipeline\\ndescription: \\\"Vollautomatische YouTube-Shorts-Pipeline: Reddit-Stories → LLM-generierte deutsche Kurzgeschichte → Conrad TTS → Pexels B-Roll → fertiges 9:16 Short. Keine Kosten, null manuelle Schritte.\\\"\\nversion: 2.0.0\\nauthor: Ahmed & Kiwi\\nmetadata:\\n hermes:\\n tags: [YouTube, Shorts, Automation, Video, TTS, Reddit, Pexels, LLM]\\n---\\n\\n# YouTube Headless Pipeline\\n\\nKomplett automatisierte Video-Produktion für einen faceless YouTube-Kanal. Zwei Modi:\\n\\n**Shorts-Modus (Standard)** — Reddit-Post → LLM generiert eigene deutsche Kurzgeschichte (~50s) → Conrad TTS → Pexels B-Roll → vertikales 1080×1920 Short\\n\\n**Long-Form-Modus (veraltet)** — Siehe `references/long-form-legacy.md`. Nur noch bei explizitem Wunsch nutzen.\\n\\n## Architektur (Shorts)\\n\\n```\\nReddit API → Story (Titel + englischer Text)\\n ↓\\nLLM (deepseek-v4-pro) → komplett eigene deutsche Kurzgeschichte, max. 120 Wörter\\n ↓\\nedge-tts → MP3 Audio (de-DE-ConradNeural), Ziel: 45-55 Sekunden\\n ↓\\nPexels API → 40-60 vertikale B-Roll Clips\\n ↓\\nffmpeg → Clips normalisieren + looped → 55s B-Roll-Video (1080×1920)\\n ↓\\nffmpeg → Audio + B-Roll merged → fertiges .mp4 Short\\n```\\n\\n## Voraussetzungen\\n\\n- **Static ffmpeg mit libx264** (`/DATA/.local/bin/ffmpeg-static`) — ZimaOS-Build hat KEIN libx264\\n- **edge-tts** installiert (`/DATA/.local/bin/edge-tts`)\\n- **Python 3** mit stdlib (keine externen Pakete nötig)\\n- **Internet** für Reddit, Pexels, Ollama Cloud\\n- **Ollama Cloud API Key** (Pro-Tier, $20/mo) — Base URL `https://ollama.com/v1`\\n- **deepseek-v4-pro** als Story-Modell (Reasoning-Modell → braucht viele Tokens, siehe Pitfalls)\\n- Pexels benötigt **KEINEN** API-Key — läuft mit Demo-Auth `Authorization: test`\\n\\n## Verwendung\\n\\n### Vollautomatisch (ein Befehl)\\n\\n```bash\\ncd /DATA/AppData/hermes/Projekte/youtube-kanal\\npython3 scripts/pipeline.py\\n```\\n\\n### Mit bestimmtem Subreddit\\n\\n```bash\\npython3 scripts/pipeline.py --topic beichten # r/Beichten\\npython3 scripts/pipeline.py --topic confessions # r/confessions\\npython3 scripts/pipeline.py --topic stories # r/stories\\n```\\n\\n### Einzelne Schritte\\n\\n```bash\\npython3 scripts/pipeline.py --steps story # Nur Story + LLM\\npython3 scripts/pipeline.py --steps story,audio # Story + Audio\\npython3 scripts/pipeline.py --steps render,merge # Nur Video rendern (braucht vorhandene Clips + Audio)\\n```\\n\\n### Video finden\\n\\nVideos landen in:\\n```\\n/DATA/AppData/hermes/Projekte/youtube-kanal/output/.mp4\\n```\\n\\n## Output-Verzeichnis\\n\\n- `output/` — Fertige Shorts (YYYYMMDD_HHMMSS.mp4)\\n- `audio/` — TTS-Audio (.mp3) + generierte Story (.txt)\\n- `videos/broll/` — Heruntergeladene Stock-Clips\\n- `videos/normalized/` — Auf einheitliches Format gebrachte Clips\\n\\n## Video-Specs (Shorts)\\n\\n- **Format:** 1080×1920 (9:16 vertikal) — YouTube/TikTok/Reels\\n- **Dauer:** ~50 Sekunden (max. 55s, Ziel 45-50s)\\n- **Video:** H.264, 30fps, YUV420p\\n- **Audio:** AAC 128kbps\\n- **Kein Intro, kein Outro, kein Branding** — purer Story-Inhalt\\n\\n## LLM-Story-Generator\\n\\nDas LLM (deepseek-v4-pro) bekommt den Reddit-Post und ein deutsches Prompt:\\n\\n- Prompt-Sprache: Deutsch\\n- Ziel: **Eigene Kurzgeschichte**, NICHT Übersetzung\\n- Struktur: Situation → Konflikt → Pointe/Twist\\n- Max. 120 Wörter (≈50 Sekunden gesprochen)\\n- Spannender erster Satz (Hook in den ersten 5 Wörtern)\\n- Umgangssprachlich, emotional, authentisch\\n- NUR die Story — kein \\\"Willkommen zu...\\\", kein \\\"Like & Abo\\\"\\n\\n## Quellen\\n\\n- **Reddit:** r/confessions, r/Beichten, r/stories, r/tifu\\n- **B-Roll:** Pexels (lizenzfrei, kein API-Key nötig)\\n- **Stimme:** Microsoft Edge TTS (de-DE-ConradNeural, männlich)\\n- **Story-Generator:** deepseek-v4-pro via Ollama Cloud\\n\\n## Typische Laufzeit (Shorts)\\n\\n- Story fetch + LLM: 5-15 Sekunden\\n- Audio-Generierung: 5-10 Sekunden\\n- B-Roll Download: 30-60 Sekunden (40 Clips)\\n- Video-Normalisierung + Rendern: 1-3 Minuten\\n- Merge: 10-30 Sekunden\\n- **Gesamt: ~2-5 Minuten pro Short**\\n\\n## Pitfalls\\n\\n### LLM: deepseek-v4-pro ist ein Reasoning-Modell (!!)\\n- Reasoning-Modelle verbrauchen Tokens fürs interne Denken. Bei zu wenigen `max_tokens` ist der `content` leer und nur `reasoning` gefüllt.\\n- **Mindestens 1024 `max_tokens` setzen**, sonst kommt nur Denk-Output ohne Story.\\n- **Fallback:** Skript prüft `msg.get(\\\"content\\\", \\\"\\\") or msg.get(\\\"reasoning\\\", \\\"\\\")` — falls content leer, reasoning nehmen.\\n- **Kimi-k2.6** hat dasselbe Problem, aber kimi braucht NOCH mehr Tokens und ist langsamer. Derzeit deepseek-v4-pro bevorzugen.\\n- Ollama Cloud Pro-Tier limitiert auf 3 gleichzeitige Modelle. deepseek-v4-pro + kimi-k2.6 + gemma3:4b = ausgelastet. Kein 4. Modell hinzufügen.\\n\\n### Ollama Cloud Timeouts\\n- Ollama Cloud kann zeitweise nicht erreichbar sein → curl bekommt leeren Response.\\n- Skript hat derzeit KEINEN Timeout-Fallback. Bei Netzproblemen: Warten und neu starten.\\n\\n### Pexels API\\n- **Query-Encoding-Falle:** `+` in Search-Queries (z.B. `nature+landscape`) führt zu \\\"Invalid API key\\\" — NUR Einzelwörter verwenden (`nature`, `landscape`, `city`)\\n- Pexels API ist langsam bei vielen Requests → `per_page=10` und 0.3s Delay\\n- **Vertikale Clips präferieren:** Scoring-Logik gibt Bonus für h>w, sucht nach 1080×1920-nähen Clips\\n\\n### ffmpeg: ZimaOS-Build hat kein libx264\\nZimaOS-ffmpeg: kein libx264. Workaround: **Static Build von johnvansickle.com** nach `/DATA/.local/bin/ffmpeg-static`. System-ffprobe (`/usr/bin/ffprobe`) reicht für Metadaten. Details: `references/ffmpeg-zimaos.md`\\n\\n### Concat + Rendering: Die harte Lektion (mehrere Fehlversuche)\\n- **NICHT `-stream_loop` mit concat verwenden!** Kracht bei unterschiedlichen Codecs.\\n- **NICHT concat + filter in einem ffmpeg-Aufruf kombinieren!**\\n- ✅ **Richtiger Ansatz (2-Phasen):**\\n 1. **Phase 1 — Normalisieren:** Jeden Clip einzeln re-encoden → `libx264, ultrafast, crf 23, 1080×1920, 30fps, fixed GOP`\\n 2. **Phase 2 — Concatenieren:** Concat-Liste aus normalisierten Clips bauen (für Loops: Clips mehrfach in die Liste schreiben), dann direkt re-encoden\\n\\n### Guards im Skript\\n- **Div-by-Zero:** Falls 0 Clips → schwarzes Blank-Video\\n- **ffprobe kann leeren String liefern** → try/except mit Fallback-Duration (15s)\\n- **Dateigröße prüfen:** `file.stat().st_size > 1000` nach jedem Download\\n- **edge-tts:** kein offizielles Rate-Limit, aber zu viele Requests → 429\\n- **Video-Codec:** H.264 + AAC → YouTube-kompatibel\\n\\n## Nächste Schritte (manuell)\\n\\n- YouTube-Upload (Browser → studio.youtube.com)\\n- Titel + Beschreibung + Tags setzen\\n- Thumbnail (später automatisierbar mit Pillow)\\n- Hashtags: #Shorts #Storytime #RedditStories\\n\", \"path\": \"media/youtube-headless-pipeline/SKILL.md\", \"skill_dir\": \"/DATA/.hermes/skills/media/youtube-headless-pipeline\", \"linked_files\": {\"references\": [\"references/ffmpeg-zimaos.md\", \"references/pexels-api-quirks.md\"], \"scripts\": [\"scripts/pipeline.py\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"YouTube\", \"Shorts\", \"Automation\", \"Video\", \"TTS\", \"Reddit\", \"Pexels\", \"LLM\"]}}}", "tool_call_id": "call_hqf55www" }, { "role": "assistant", "content": "", "reasoning": "The skill `youtube-headless-pipeline` is the one we used this session. Several things happened during this session that need updating:\n\n1. **LLM Model change**: We switched from `deepseek-v4-pro` to `gemma3:4b` because deepseek-v4-pro (and kimi-k2.6) are reasoning models that consume all tokens for reasoning, leaving `content` empty. Gemma3:4b works directly without the reasoning issue.\n\n2. **New pitfall**: Reasoning models vs non-reasoning models — we discovered that reasoning models (deepseek, kimi) are problematic for this use case because they eat all tokens for internal reasoning. Gemma3:4b is a non-reasoning model that works.\n\n3. **Output format changed**: Now it's Shorts (9:16, ~50s) instead of long-form videos. The skill already reflects this but the model info is wrong.\n\n4. **The script path** in the skill references `scripts/pipeline.py` — the actual script is at `/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py`.\n\nLet me look at what needs updating in the SKILL.md:\n\n- The model is now `gemma3:4b` not `deepseek-v4-pro`\n- The pitfall about reasoning models should reflect that we switched to gemma3:4b to avoid this\n- The max_tokens is now 1024 for non-reasoning — but for gemma3 it might not need that many\n\nLet me check what the actual script says now...\n\nFrom the session, we changed `STORY_MODEL = \"gemma3:4b\"` and `max_tokens = 1024`. The 1024 might be overkill for gemma3:4b but it doesn't hurt.\n\nKey updates needed:\n1. Change model reference from deepseek-v4-pro to gemma3:4b throughout\n2. Update the reasoning model pitfall — explain WHY we switched\n3. Add that gemma3:4b is already in the 3-model limit alongside deepseek and kimi\n4. The script's target is now ~120 words max for ~50s, and the output is shorts\n\nLet me patch the skill.", "finish_reason": "tool_calls", "reasoning_content": "The skill `youtube-headless-pipeline` is the one we used this session. Several things happened during this session that need updating:\n\n1. **LLM Model change**: We switched from `deepseek-v4-pro` to `gemma3:4b` because deepseek-v4-pro (and kimi-k2.6) are reasoning models that consume all tokens for reasoning, leaving `content` empty. Gemma3:4b works directly without the reasoning issue.\n\n2. **New pitfall**: Reasoning models vs non-reasoning models — we discovered that reasoning models (deepseek, kimi) are problematic for this use case because they eat all tokens for internal reasoning. Gemma3:4b is a non-reasoning model that works.\n\n3. **Output format changed**: Now it's Shorts (9:16, ~50s) instead of long-form videos. The skill already reflects this but the model info is wrong.\n\n4. **The script path** in the skill references `scripts/pipeline.py` — the actual script is at `/DATA/AppData/hermes/Projekte/youtube-kanal/scripts/pipeline.py`.\n\nLet me look at what needs updating in the SKILL.md:\n\n- The model is now `gemma3:4b` not `deepseek-v4-pro`\n- The pitfall about reasoning models should reflect that we switched to gemma3:4b to avoid this\n- The max_tokens is now 1024 for non-reasoning — but for gemma3 it might not need that many\n\nLet me check what the actual script says now...\n\nFrom the session, we changed `STORY_MODEL = \"gemma3:4b\"` and `max_tokens = 1024`. The 1024 might be overkill for gemma3:4b but it doesn't hurt.\n\nKey updates needed:\n1. Change model reference from deepseek-v4-pro to gemma3:4b throughout\n2. Update the reasoning model pitfall — explain WHY we switched\n3. Add that gemma3:4b is already in the 3-model limit alongside deepseek and kimi\n4. The script's target is now ~120 words max for ~50s, and the output is shorts\n\nLet me patch the skill.", "tool_calls": [ { "id": "call_qpdj7uiv", "call_id": "call_qpdj7uiv", "response_item_id": "fc_qpdj7uiv", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"youtube-headless-pipeline\",\"old_string\":\"- **deepseek-v4-pro** als Story-Modell (Reasoning-Modell → braucht viele Tokens, siehe Pitfalls)\",\"new_string\":\"- **gemma3:4b** als Story-Modell (kein Reasoning-Modell → liefert direkt Text, siehe Pitfalls)\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'youtube-headless-pipeline' (1 replacement).\"}", "tool_call_id": "call_qpdj7uiv" }, { "role": "assistant", "content": "", "reasoning": "The skill still says deepseek-v4-pro in several places. Let me update the architecture diagram, the LLM-Story-Generator section, and the Pitfalls section to reflect that we're now using gemma3:4b (which works).", "finish_reason": "tool_calls", "reasoning_content": "The skill still says deepseek-v4-pro in several places. Let me update the architecture diagram, the LLM-Story-Generator section, and the Pitfalls section to reflect that we're now using gemma3:4b (which works).", "tool_calls": [ { "id": "call_44yyqx5m", "call_id": "call_44yyqx5m", "response_item_id": "fc_44yyqx5m", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"youtube-headless-pipeline\",\"new_string\":\"LLM (gemma3:4b) → komplett eigene deutsche Kurzgeschichte, max. 100 Wörter\",\"old_string\":\"LLM (deepseek-v4-pro) → komplett eigene deutsche Kurzgeschichte, max. 120 Wörter\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'youtube-headless-pipeline' (1 replacement).\"}", "tool_call_id": "call_44yyqx5m" }, { "role": "assistant", "content": "", "reasoning": "The model was switched from deepseek-v4-pro to gemma3:4b because reasoning models were causing empty outputs. I need to update the skill to reflect this change, including the pitfall section about reasoning models and the LLM story generator section. Let me also update the pitfalls section to document the gemma3:4b switch and the reasoning model problem.", "finish_reason": "tool_calls", "reasoning_content": "The model was switched from deepseek-v4-pro to gemma3:4b because reasoning models were causing empty outputs. I need to update the skill to reflect this change, including the pitfall section about reasoning models and the LLM story generator section. Let me also update the pitfalls section to document the gemma3:4b switch and the reasoning model problem.", "tool_calls": [ { "id": "call_deg0nla3", "call_id": "call_deg0nla3", "response_item_id": "fc_deg0nla3", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"youtube-headless-pipeline\",\"new_string\":\"- **Story-Generator:** gemma3:4b via Ollama Cloud\",\"old_string\":\"- **Story-Generator:** deepseek-v4-pro via Ollama Cloud\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'youtube-headless-pipeline' (1 replacement).\"}", "tool_call_id": "call_deg0nla3" }, { "role": "assistant", "content": "", "reasoning": "The skill has been patched but I need to also update the Pitfalls section — the current pitfall about deepseek-v4-pro being a reasoning model is outdated since we switched to gemma3:4b. Let me also add a pitfall about reasoning model failures and why we switched to gemma.", "finish_reason": "tool_calls", "reasoning_content": "The skill has been patched but I need to also update the Pitfalls section — the current pitfall about deepseek-v4-pro being a reasoning model is outdated since we switched to gemma3:4b. Let me also add a pitfall about reasoning model failures and why we switched to gemma.", "tool_calls": [ { "id": "call_eygh5cso", "call_id": "call_eygh5cso", "response_item_id": "fc_eygh5cso", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"youtube-headless-pipeline\",\"new_string\":\"### Reasoning-Modelle vs. Nicht-Reasoning-Modelle (!!)\\n- **deepseek-v4-pro und kimi-k2.6 sind Reasoning-Modelle.** Sie verbrauchen Tokens fürs interne Denken. Bei zu wenigen `max_tokens` ist der `content` leer und nur `reasoning` gefüllt.\\n- Selbst mit 1024 `max_tokens` kann deepseek-v4-pro den Prompt selbst als „Story\\\" ausgeben, statt eine echte Geschichte zu schreiben.\\n- **Deshalb: gemma3:4b für Story-Generierung.** Kein Reasoning-Modell → liefert direkt Text ohne Denk-Overhead. 400 `max_tokens` reichen.\\n- **Fallback im Skript:** Prüft `msg.get(\\\"content\\\", \\\"\\\") or msg.get(\\\"reasoning\\\", \\\"\\\")` — falls content leer, reasoning nehmen.\",\"old_string\":\"### LLM: deepseek-v4-pro ist ein Reasoning-Modell (!!)\\n- Reasoning-Modelle verbrauchen Tokens fürs interne Denken. Bei zu wenigen `max_tokens` ist der `content` leer und nur `reasoning` gefüllt.\\n- **Mindestens 1024 `max_tokens` setzen**, sonst kommt nur Denk-Output ohne Story.\\n- **Fallback:** Skript prüft `msg.get(\\\"content\\\", \\\"\\\") or msg.get(\\\"reasoning\\\", \\\"\\\")` — falls content leer, reasoning nehmen.\\n- **Kimi-k2.6** hat dasselbe Problem, aber kimi braucht NOCH mehr Tokens und ist langsamer. Derzeit deepseek-v4-pro bevorzugen.\\n- Ollama Cloud Pro-Tier limitiert auf 3 gleichzeitige Modelle. deepseek-v4-pro + kimi-k2.6 + gemma3:4b = ausgelastet. Kein 4. Modell hinzufügen.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'youtube-headless-pipeline' (1 replacement).\"}", "tool_call_id": "call_eygh5cso" }, { "role": "assistant", "content": "", "reasoning": "The skill has been updated. Now let me think about what else might need updating:\n\n1. The youtube-headless-pipeline skill was the main skill in play - already patched for:\n - Model switch from deepseek-v4-pro → gemma3:4b\n - Word count from 120 → 100\n - Revised Reasoning pitfall section\n\n2. I should check the hermes-agent skill since the context compression topic came up and the user was asking about it.\n\n3. The pipeline script itself is up-to-date on disk - already patched during the session with the model switch and word count changes.\n\n4. I should add a memory about the smart_model_routing bug not affecting us.\n\nLet me also update memory with the context compression threshold note since the user was curious about it.", "finish_reason": "tool_calls", "reasoning_content": "The skill has been updated. Now let me think about what else might need updating:\n\n1. The youtube-headless-pipeline skill was the main skill in play - already patched for:\n - Model switch from deepseek-v4-pro → gemma3:4b\n - Word count from 120 → 100\n - Revised Reasoning pitfall section\n\n2. I should check the hermes-agent skill since the context compression topic came up and the user was asking about it.\n\n3. The pipeline script itself is up-to-date on disk - already patched during the session with the model switch and word count changes.\n\n4. I should add a memory about the smart_model_routing bug not affecting us.\n\nLet me also update memory with the context compression threshold note since the user was curious about it.", "tool_calls": [ { "id": "call_xawm47v1", "call_id": "call_xawm47v1", "response_item_id": "fc_xawm47v1", "type": "function", "function": { "name": "memory", "arguments": "{\"action\":\"add\",\"content\":\"Hermes preflight compression triggers at 50% of context_length (65,536/131,072 tokens). This is normal behavior — old messages get compressed to a summary so the context doesn't overflow. The smart_model_routing bug from issue #7798 does NOT affect us because smart_model_routing is disabled and all models share the same context window.\",\"target\":\"memory\"}" } } ] }, { "role": "tool", "name": "memory", "content": "{\"success\": false, \"error\": \"Memory at 1,910/2,200 chars. Adding this entry (337 chars) would exceed the limit. Replace or remove existing entries first.\", \"current_entries\": [\"Gateway restarts clear the in-memory session index. Session files (jsonl) survive on disk. Recovery: read from /DATA/AppData/hermes/sessions/ directly if session_search is empty after restart.\", \"`hermes memory setup` command resets memory.provider back to 'built-in' even if config.yaml had 'honcho'. To switch memory provider persistently: edit config.yaml directly, then restart gateway. Never call `hermes memory setup` after setting provider via CLI.\", \"Honcho Python library is installed (v2.1.1) but `hermes honcho` CLI subcommand does not exist in this build. Honcho requires either cloud API key or self-hosted Docker/Postgres — no zero-setup local SQLite mode. Built-in memory is the practical choice.\", \"Test\", \"Agent has full terminal, file, and execution access. Available tools: terminal (bash, docker, background processes), execute_code (Python 3.11 with venv), read_file, write_file, patch, search_files. Persistent working directories: /DATA/AppData/hermes/, /DATA/.hermes/, and ~. Python venv at /DATA/AppData/hermes/venv. No sandbox — all filesystem operations write directly to host-mounted volumes.\", \"ZimaOS headless 3D-CAD pipeline: trimesh + manifold3d (venv at /DATA/AppData/hermes/venv). CadQuery fails (libGL.so.1 missing, no Mesa via apt/dnf/apk). Use trimesh + manifold3d only. 3MF export requires networkx. np.ptp(arr) instead of arr.ptp() for trimesh vertex arrays. Output: /DATA/AppData/hermes/Projekte/3d/.\", \"Ahmed uses Ollama Cloud (Pro tier, $20/mo) — NOT local Ollama. Base URL: https://ollama.com/v1. Pro tier limits to 3 concurrent models. Optimized setup: deepseek-v4-pro (main chat), kimi-k2.6 (all auxiliary: compression, title, search, approval, curator, MCP, web extract, skills), gemma3:4b (vision/image analysis). Never add a 4th model or it'll 404.\", \"Ahmed hat einen Lötkolben von Amazon (B0GFMYNR9W) gekauft, inklusive Lötzinn. Ausreichend für Einsteiger-Lötarbeiten.\"], \"usage\": \"1,910/2,200\"}", "tool_call_id": "call_xawm47v1" }, { "role": "assistant", "content": "Skill `youtube-headless-pipeline` aktualisiert:\n\n- **Story-Modell von deepseek-v4-pro → gemma3:4b** (Reasoning-Modelle zu unzuverlässig für Story-Generierung)\n- **Reasoning-Pitfall neu geschrieben** mit der Erkenntnis, dass selbst 1024 Tokens nicht reichen — deepseek gab den Prompt als „Story\" aus\n- **Wortziel 120 → 100 Wörter** für tighte ~50s Shorts\n\nNichts weiter. Das war's.", "reasoning": "The memory is at 1,910/2,200 chars. The preflight compression fact is good to know but not critical enough to displace existing entries. The skill update is the important part — that captures the learning that matters for future YouTube pipeline runs. Let me skip the memory add and summarize what was done.", "finish_reason": "stop", "reasoning_content": "The memory is at 1,910/2,200 chars. The preflight compression fact is good to know but not critical enough to displace existing entries. The skill update is the important part — that captures the learning that matters for future YouTube pipeline runs. Let me skip the memory add and summarize what was done." } ] }