Tools

A NAS owner’s account of a firmware bug that dropped a healthy disk on every boot, and how I used Claude Code to reverse-engineer the fix out of the closed-source firmware. A quick note on this post before we start: the NAS is mine, the problem was mine, and the tests (and the swearing) were mine. The deep reverse-engineering, disassembling the firmware’s own binaries and tracing data flow instruction by instruction, was done by Claude Code, which I drove and second-guessed throughout. So when you read “Claude found,” that is literally what happened. When you read “I rebooted and it was still broken,” that is me being the reality check. It was a genuine back-and-forth, and it is worth being honest about who did which part. ...

I gave opencode a single-paragraph spec for “an HTML canvas where colored circles attract similar hues and repel opposites,” and pointed it at unsloth’s quant of Qwen3.6-35B-A3B (UD-Q8_K_XL, ~3B active params) running locally. About a dozen steering turns later, fixing bugs and nudging the behavior, I had a working single-file demo. That is the part worth noting. Not one-shot, not magic: a small MoE that fits on one GPU, driven through an agentic loop, can converge on a real interactive artifact in a reasonable session. Local coding agents are becoming a usable workflow, not just a demo. ...

Google launched Gemini 3.1 Flash Live via the Live API for building real-time voice and vision agents. The model processes continuous audio, video, and text streams to deliver immediate spoken responses with acoustic nuance detection and 90+ language support. Key improvements over Gemini 2.5 Flash Native Audio: Better noise filtering in real-world environments Stronger adherence to complex system instructions More natural dialogue with improved latency Thinking capability via thinkingLevel (minimal/low/medium/high) instead of thinkingBudget The model outputs native audio (no STT+TTS pipeline) with a 128k context window. It supports synchronous function calling, Google Search grounding, and video input alongside audio. ...

GitAgent proposes that your git repository is your agent. Two required files - agent.yaml (manifest) and SOUL.md (identity) - define the agent. Everything else - skills, tools, memory, compliance artifacts - is optional structure layered on top. The interesting part is the supervision model: when an agent updates its memory or acquires a new skill, the change becomes a git commit or PR. Human reviewers can diff the agent’s personality changes like any code review. If behavior drifts, git revert brings it back. ...

Google released an open-source MCP server for Google Colab. Any MCP-compatible agent - Claude Code, Gemini CLI, or a custom agent - can now programmatically control a Colab notebook: create cells, write and execute code, install dependencies, rearrange content. The setup is one config block: "mcpServers": { "colab-mcp": { "command": "uvx", "args": ["git+https://github.com/googlecolab/colab-mcp"], "timeout": 30000 } } The motivation is concrete: developers were copying code from their terminals into Colab cells to run or visualize things. That context switch kills flow. With this server, the agent writes directly into an open notebook - you get a reproducible, executable artifact in the cloud instead of a code snippet in your terminal. ...

The problem with agent guardrails that live inside the agent: a compromised agent can override them. Claude Code and Cursor ship with internal safety prompts, but those protections are inside the same process they’re supposed to guard. A prompt injection or a bad third-party skill has access to the same runtime. NVIDIA OpenShell moves the enforcement point outside. It wraps any agent in an isolated container with YAML-defined policies the agent cannot read or modify. Network access is deny-by-default and hot-reloadable; filesystem and process constraints are locked at creation. The agent can’t escalate privileges because the kernel won’t allow it - not because the agent was told not to. ...

Unsloth Studio bundles local inference, fine-tuning, and model export into a single no-code web UI. One curl command installs it; then you can run GGUF or safetensor models on Mac, Windows, or Linux without writing any code. The training side is the main draw: 2x faster fine-tuning with 70% less VRAM across 500+ model families (text, vision, TTS, embeddings). LoRA, FP8, and full fine-tuning all work on NVIDIA hardware, with multi-GPU support already in. ...

OpenViking abandons traditional RAG vector storage and uses a filesystem paradigm instead. It organizes agent context (memories, resources, skills) under viking:// URIs with a three-tier structure: L0 (Abstract): One-sentence summary for quick retrieval L1 (Overview): Core information and usage scenarios L2 (Details): Full original data, loaded on demand This enables directory recursive retrieval that locks high-score directories first, then refines content exploration. The retrieval trajectory is fully observable, letting users see exactly how context is being accessed. ...

Mastra’s agent HTTP API returns a JSON structure with steps, each containing content items typed as reasoning, tool-call, tool-result, and text. The raw output is dense. Start by exploring it: # Hit the API and see raw structure http localhost:4111/api/agents/weather-agent/generate \ messages[0]="what's the weather in montreal?" | jq . # Get just the final answer http localhost:4111/api/agents/weather-agent/generate \ messages[0]="what's the weather in montreal?" | jq -r '.text' # Explore what's inside steps http localhost:4111/api/agents/weather-agent/generate \ messages[0]="what's the weather in montreal?" | jq '.steps[].content[] | .type' # "reasoning" # "tool-call" # "tool-result" # "text" # "reasoning" # "text" # See what fields each type has http localhost:4111/api/agents/weather-agent/generate \ messages[0]="what's the weather in montreal?" | jq '.steps[].content[] | select(.type == "tool-call")' Once the structure is clear, pipe through jq -r with inline ANSI escape sequences to colorize each piece: ...

If a custom OpenClaw skill doesn’t show up in openclaw skills list and the agent can’t see it either, the SKILL.md frontmatter is likely the culprit. OpenClaw fails silently, so the debugging feedback is minimal. Two things must be right. First, any name or description containing a colon must be wrapped in double quotes, otherwise YAML interprets the colon as a key-value separator and the parse fails. Second, the frontmatter must include an openclaw metadata block declaring the emoji icon and any required binaries or environment variables. Without it, OpenClaw won’t register the skill at all. ...