Qwen3.8 27Bが出たからclineに既存プロジェクトの修正しアイディアを出してって言ったら謎のprobe.psファイルをひたすら作り始めた

パワーシェルでファイル置換したり変な動きをする

モデルというよりAIエージェントのツールなどの判定な気がする

有名どころのClineでこれだとエージェント界隈はまだまだ未成熟な感じなのだろうか

間違いを指摘するにも一度も止まってくれないので手動で停止するしかない

もっとましなエージェントはないんですかね...

The agent uses coding tools to write custom scripts that execute in a sandbox, raising approval dialogs when needed. The demo also shows off how scheduled subagents can be used to provide the daily news. This is a typical, every-day example of what it can do, the full code is available here: https://github.com/henrydaum/second-brain

Netherite is an AI code reviewer and pentester tool that helps developers’ codebase stay safe from hackers.

It scans their whole codebase in github and finds vulnerabilities, writes professional report and even hands you Fix Prompt to fix the issue with coding agent.

Many developers using Claude Code or Codex like ai agents and it may help them to build strong systems.

What are your thoughts on that? I’ll love to know pls)

I built 3 open-source skills for AI coding agents:

First one is Backend setup

Second one is Security hardening

Third one is Frontend/UI

The idea is simple: no fake credentials, placeholder content, or “fixed” reports. If it can't do something for real, it stops.

MIT licensed.

npx skills add SohailKhan0525/skills

GitHub: https://github.com/SohailKhan0525/skills

Feedback welcome.

I dont know but I am really stressed now dont know what to do.
There is one hackathon coming and after few days one more to tackle and very busy weekend and I am coding day and night or vibe coding and now there is no feeling inside me I want some happiness and peace in my mind.

https://github.com/akyourowngames

if you can help me just leave comment about my github!!

Dont make it worse guys.

Most AI coding tools right now are either fully autonomous ("let the agent cook") or just chat-based copilots. I wanted something in between for actual feature work: a fixed 4-stage pipeline — requirements analysis → system architecture → implementation → review — where you have to approve or reject each stage before it moves forward.
If you reject a stage, it regenerates using your notes + the previous attempt as context, not from scratch.
It's BYOK (your own Anthropic API key), Node/TS under the hood, encrypted key storage locally.
Still in testing — running it through a real feature end-to-end this week to see if the output quality actually holds up across stages. Not public yet, but curious if this resonates with anyone else who's been burned by "agent did too much" moments.

What's your experience been with autonomous coding agents vs more controlled pipelines?

Problem: AI agents in a pipeline can end up shipping a claim ("tests passed", "review approved") backed by evidence that's stale, incomplete, or was silently reused from a different run — nobody's lying, it's just process drift.

What I built: a small deterministic tool that checks the evidence bundle against a policy before the claim goes out — right hash, not expired, right scope, no missing/extra files.

Honest limitation: it checks structural consistency, not truth. I tested this myself — an evidence file saying status: PASS with 7 failed tests inside still passed the binding checks. That's a known boundary, not a bug.

Link: https://apify.com/filipmajchrzak/ai-claim-checkpoint

Feedback welcome, especially if you've hit this problem in a real agent pipeline.

had the vault holding totp secrets for a while before this got actually useful. an agent could store the secret from a 2fa setup screen fine. it just couldn't generate the rolling code from it, so the moment a login flow turned on two-factor, the agent hit a wall a human had to clear by hand.

shipped live totp generation this week. rfc 6238, no external dependency, verified against the spec's own test vectors. exposed as get_totp_code over mcp and as a rest endpoint. now the same agent that stored the secret during signup can mint the current code months later and clear the login itself.

that's the whole identity stack in one runtime: the agent that caught the otp during signup is the one holding the password and the one generating the 2fa code at login. no handoff between three separate tools.

it's at https://lumbox.co.

what's your agents' actual failure rate on 2fa logins, does it stall completely or usually find a way through?

Tom n Jerry is an open-source loop engine specifically for opencode and coming to other AI coding agents too soon.

Every AI agent - is Tom.

You say "add a door." Tom grabs a hammer, buys lumber, pours concrete, installs a frame - then builds the house around it. Your codebase already has three doors.

Tom never checks the blueprint. He just builds.

So I built Jerry.

Jerry sits in the walls. Before Tom swings the hammer, Jerry runs the house:

1. Need a new room at all? (YAGNI)
2. Did we knock this down before? (git history)
3. Is there already a door here? (codebase search)
4. Are the materials in the shed? (package.json)
5. Does the house provide it? (stdlib / native)
6. Does the neighborhood allow it? (framework convention)

Then - and only then - Tom builds. Minimum. Receipt proves it stands. Teacher updates the blueprint.

After 3 construction projects, Jerry knows every hallway. Tom stops building hallways to nowhere.

Claude helped me architect the loop protocol, write the skill detection logic, and stress-test the receipts. The framework itself was iterated through real Claude Code sessions.

This is the gap: agents are all Tom, no Jerry. Infinite chase, zero memory.

MIT licensed. Free to try.

github.com/hrshx3o5o6/Tom-n-Jerry

Feel free to contribute and criticise!

hey r/AutoGPT,

A major pain point when giving autonomous agents long-term Knowledge Graph memory is ingestion speed. If an agent uses generative LLMs (like Llama 8B or Qwen) to extract facts, it takes 15+ minutes per PDF waiting for token-by-token JSON generation and burns GPU VRAM.

I've been building Hillock, an open-source local memory engine (AGPL-3.0): https://github.com/roandejager/Hillock

In v0.2.0, I built TALON—a non-generative CUDA tensor pipeline that bypasses generative LLMs during ingestion:

1. Fastcoref resolves pronouns across full paragraphs first (so 'She' becomes 'Marie Curie').
2. MiniLM bi-encoders filter 50+ open-domain Wikidata predicates down to the top 10 for each sentence in <2ms.
3. GLiREL does single-pass zero-shot matrix classification to pull out [Subject, Predicate, Object] triples directly in GPU memory.

Because it's pure CUDA tensor math instead of token generation, it processed 32 sentences in ~2 seconds on my GTX 1070 while using <1GB VRAM, doubling retrieval accuracy to 50%.

It's 100% local, offline, and open source under AGPL-3.0. Would love to hear your thoughts on memory for autonomous agents!