
AI Engineer at a VC backed one person company | Founder @AntlerGlobal | prev @microsoft |
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Joined November 2023
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Again 80% of this is common knowledge
so, here's the 20% juice you need from this article
1. Don't be lazy to let Claude interview you if you don't know what you're doing. Then, use the interview notes from Claude to start a new session.
2. When using /goal, you must tell Claude how to verify it with clear criteria. (lint+unit+integration+E2E tests pass, user scenario simulations etc)
3. When using long running /goal runs, explicitly tell the model to use a different model to judge itself on what it's doing
4. Parallel Claude Code sessions are more useful than a single session when they can finish without you. (Eg: Running multiple /goal sessions)
Follow for more!
Kinda wild ngl
Age of employment is coming to an end. Age of building companies is the future. Taking us back to merchant era.
This is actually Cohere hyping things to tell people it’s still around. Sadly, no one cares
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
80% of this is common knowledge.
The 20% juice is
- Change your PR Template to contain Risk, How to revert
- Classify PRs to figure out how many review cycles are needed between Agent PR Author and Agent Code Reviewer based on blast radius and risk
- Steal the modern PR etiquette from the article
This is genuinely concerning
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: cohere.com/blog/concerning-b…
We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence.
block.xyz/dessa
Seth retweeted
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Damn
Replying to @MKBHD
I’m not. Will use SpaceX cold gas thruster system with ultra high pressure air in a composite over-wrapped pressure vessel in place of the 2 rear seats.
Seth retweeted
We are releasing Ultramarine 1 Flash, a frontier-class model built for knowledge work. It is open-source and achieves similar performance to Fable 5, while being 60x cheaper.
It was post-trained by a team of 5 for ~$300k all-in. Available on API now, with weights releasing in 2 weeks.
Day 2 was crazy @ChampionsLeague
City’s loss was sad but damn Anderson and the new bois are so class.
Seth retweeted
Just ran Argon on my "codebase analysis" bench. Not looking good. Only model with a >50% false claim rate 🙃
Gemini 4 Argon is here. I got early access (thank you friends at Deepmind!)
It is a huge jump for Gemini, and pretty good value for the price. First every model to get a perfect score on Skatebench-private
It’s still a Gemini model though. Does weird shit regularly and I don’t feel like I can trust it at all.
Video with more thoughts coming soon :)

















