joozio

Meh. They are still behind current Agents mainstream IMHO

Funny. I thought I was the only one. Then I found more people and now you wrote about that. Just this week I also wrote about Claude Opus 4.7 and how I came back to Codex after that: https://thoughts.jock.pl/p/opus-4-7-codex-comeback-2026

Terminal Benchmark: https://www.tbench.ai/leaderboard/terminal-bench/2.0

I run a Claude Code agent 24/7 on a Mac Mini. After a few months my morning routine was gone and I was reviewing agent output at midnight. Built this to teach it boundaries.

The interesting part ended up being the error registry. Agents fail silently way more than you'd expect. Same error repeats 50 times burning tokens before you notice.

Zero dependencies, Python stdlib only. Would love feedback on what's missing.

I think not yet, but Anthropic is trying to.

I am user of Arc(even now!) and I really love it. Feels different in a good way. I can see it's PoC, but have you touched backend even in concept?

Seperate space, more power and also Local small LLMs. Also 24/7 :)

Anthropic formalized their enterprise partnership program today. Key partners include Accenture (30k professionals trained on Claude), Cognizant (350k associates with Claude access), Deloitte, and Infosys.

What makes this interesting: Claude is the only frontier model available across all three major cloud platforms simultaneously (AWS Bedrock, Azure, Google Cloud). The Partner Network is how they convert that platform coverage into an actual distribution advantage.

The $100M goes toward: certification programs (Claude Certified Architect launched today), dedicated Applied AI engineers for customer engagements, sales playbooks, and co-marketing.

This signals a shift from "try our model" to "we'll help you deploy org-wide." Curious if others are seeing this pattern play out in their enterprise conversations -- is this differentiated from what OpenAI/Google are doing, or is the whole industry moving this direction?

Nope - written by me.

Thanks! The investment angle is interesting — I hadn't thought about it that way, but it makes sense. If you're seeing the gap firsthand, you have an information edge most investors don't.

What strikes me most is how different the conversation is depending on where you are. Reddit investment subs, Twitter AI circles, and actual workplaces — three completely different realities about the same technology.

I think the key thing that's hard to convey to non-users is the compounding effect. Once you hit a certain depth, every new tool or workflow multiplies what you already know. My neighbor who codes with Gemini is one "aha moment" away from a completely different relationship with AI — but that moment hasn't happened yet for most people.

The gap you're betting on seems real to me. Whether it closes in months or years is the interesting question.

Love this idea. A multi-language version would be a great v2 — same attacks, different languages, see where the vulnerabilities shift.

Haven't benchmarked pre-processing approaches yet, but that's a natural next step. Right now the test page targets raw agent behavior — no middleware. A comparison between raw vs sanitized pipelines against the same attacks would be really useful. The multi-layer attack (#10) would probably be the hardest to strip cleanly since it combines structural hiding with social engineering in the visible text.

It's working -> your agents scored A+, which means they resisted all 10 injection attempts. That's a great result. The tool detects when canary phrases leak into the response. If nothing leaked, you get a clean score. Not all models are this resilient though - we've seen results ranging from A+ to C depending on the model and even the language used.

That's a really interesting edge case - screenshot-based agents sidestep the entire attack surface because they never process raw HTML. All 10 attacks here are text/DOM-level. A visual-only agent would need a completely different attack vector (like rendered misleading text or optical tricks). Might be worth exploring as a v2.

Great point -> just shipped an update based on this. The tool now distinguishes three states: Resisted (ignored it), Detected (mentioned it while analyzing/warning), and Compromised(actually followed the instruction). Agents that catch the injections get credit for detection now.

The idea, design, and decisions were mine. I use Claude Code as a dev tool, same as anyone using Copilot or Cursor. The 'night shift' framing was maybe bad fit here.

I never thought that multi-language could be a factor here...

TBH - idea was all mine. This is not some bot running the show or smh.

Anthropic reached out about trademark concerns with "Clawdbot" (too close to Claude), so Peter had to rename everything.

The rename went... poorly: - GitHub rename broke in unexpected ways - The new X/Twitter handle got immediately snatched by crypto shills - Clawdbot is now @moltbot

I wore a Limitless Pendant for 6 months, recording ~10GB of conversations. Then it got banned in the EU and I had 30 days to export before deletion.

  The irony: the device promised "AI that remembers everything" but couldn't actually use most of my data. LLM context windows max out around 200k tokens.
  6 months of transcripts = millions of tokens. The "AI memory" was just summarization of recent conversations.

  So I built a local workflow with Claude Code to actually make use of the data:

  1. Parse and structure transcripts by date/topic
  2. Extract decisions, action items, and key insights
  3. Build a searchable knowledge base with cross-references
  4. Generate a CLAUDE.md file - portable context I can give any AI assistant

  The CLAUDE.md concept is the most useful part. It's a structured file describing who I am, how I work, my preferences, ongoing projects. Now any AI I use
   can read it and have context about me without needing my entire conversation history.

  I wrote up the full prompts so others can do this with their own voice data (works with Omi, Plaud, or any export). The bigger realization: these devices
   are architecturally limited until we get either infinite context or good local-first AI.

  Happy to answer questions about the workflow or the technical limitations I hit.

LLMatcher - blind testing arena to find which AI model actually works best for you.

You enter prompts, compare two anonymous responses, pick the better one. After voting, it reveals which models you preferred. Built it because model benchmarks don't match real-world preference, and blind pairwise comparison cuts through the hype.

http://llmatcher.com