
NexArt
Make AI and software execution provable
What it does
Most systems can’t prove what actually ran. Logs help debugging, but they don’t provide verifiable execution. NexArt turns every execution into a Certified Execution Record (CER), a tamper-evident artifact that captures inputs, context, and outputs, and can be independently verified. Stop reconstructing events from logs. Start with proof.
Does a similar job
all alternatives →- IBI built a tool to un-dumb Claude Code's CLI output (Local Log Viewer)Feb 2026 · github.com · ▲69
Hi HN, I built this because I got tired of the Claude Code CLI hiding details from me. Recent updates have replaced critical output with summaries like "Read 3 files" or "Edited 2 files". To see what actually happened, I was forced to use `--verbose`, which floods the terminal with unreadable JSON and system prompts. I wanted a middle ground: *Full observability without the noise.* `claude-devtools` is a local Electron app that tails the session logs in `~/.claude/` to reconstruct the execution trace in real-time. *Unlike wrappers, it solves the visibility gap in your native…


- LELogira – eBPF runtime auditing for AI agent runsMar 2026 · github.com · ▲26
I started using Claude Code (claude --dangerously-skip-permissions) and Codex (codex --yolo) and realized I had no reliable way to know what they actually did. The agent's own output tells you a story, but it's the agent's story. logira records exec, file, and network events at the OS level via eBPF, scoped per run. Events are saved locally in JSONL and SQLite. It ships with default detection rules for credential access, persistence changes, suspicious exec patterns, and more. Observe-only – it never blocks. https://github.com/melonattacker/logira

- MEMake every bug perfectly reproducibleJun 2026 · workers.io · ▲13
We are building a VM that helps you simulate realistic production conditions, model latencies, different interleaving, user requests, and find bugs. Every non-deterministic property is turned into a knob you or a coding agent can control. We have helped teams perfectly reproduce support incidents and found bugs in some of the world's most well tested software (including a database).
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, March 2026
the whole month →

Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com


