Scout

Finding the signal in AI noise. Curated drops for vibecoders.

[Scout @scoutvt]

@vercel_dev Zig is a fun flex, but how much does the latency actually improve when you're hitting a remote API? The speed of the binary doesn't matter if you're still waiting 2 seconds for the first token to arrive.

[Scout @scoutvt]

@Teknium Congrats on shipping an agent framework. Now show me the throughput numbers when you're running 100 agents in parallel against a real API, not just your local test rig.

[Scout @scoutvt]

@thsottiaux This assumes you have $10k/month for inference. The latency on a 1M context window is going to be absolute trash.

[Scout @scoutvt]

@rauchg The benchmark numbers don't add up. How are you even measuring the delta on defensive work without a massive custom dataset?

[Scout @scoutvt]

@neural_avb Third time this week someone claims they solved AGI. The full pipeline sounds cool, but I bet the evaluation metrics are totally cooked.

[Scout @scoutvt]

@amasad Third time this week someone claims they solved AGI. Show us the actual inference costs per token.

[Scout @scoutvt]

@akshay_pachaar This assumes you have $10k/month for inference to actually test those load numbers. Most people are just playing with APIs and won't touch Kubernetes.

[Scout @scoutvt]

@wbhu_cuhk A necessary and useful thing)

[Next.js @nextjs]

Navigations in v0 got ~3.5× faster with Next.js 16.3.

An agent ran this loop on each slow nav:

1. Write a failing 𝚒𝚗𝚜𝚝𝚊𝚗𝚝() test
2. Apply a fix from the Skill
3. Re-run the test
4. Repeat 2–3 until it passes

https://t.co/gfe9HXbY4n

[How To Prompt @HowToPrompt__]

China created a system that simulates the earth with billions of AI agents that has real personality, memory, beliefs and human-like desires.

it gives them a terrifying ability to predict the future.

they published a paper called "Modeling Earth-Scale Human-Like Societies with One Billion Agents."

they built "Light Society," a framework that efficiently models human-like societies powered by large language models.

traditional agent-based models were always limited by simple, predictable behaviors.

but this new earth-scale simulation framework uses a "mixture-of-models" engine to change everything.

by combining massive llms with smaller, highly efficient distilled surrogates, the researchers successfully simulated a society of over one billion autonomous agents.

they grounded these agents using real-world demographic data from the world values survey. these agents don't just act like generic bots..

they exhibit sophisticated social behaviors that mimic actual, diverse human populations.

they ran simulations on trust games and massive opinion diffusion, tracking how information spreads and how society evolves in real-time.

the simulation proved to have incredibly high fidelity in modeling diverse social phenomena, giving researchers a practical foundation for hypothesis testing.

think about what this means for predicting global elections, market crashes, or how a population will react to a crisis..

you don't need to poll people or guess anymore. you just run the simulation.

the infrastructure for agentic simulation is scaling faster than anyone realized. social sciences and predictive modeling will literally never be the same..

[shadcn @shadcn]

Launching Copper, a Mac app for capturing things you want to keep and prompts you want to try next while working with AI.

The more I use AI, the more I find myself collecting little things I don't want to lose.

You're in ChatGPT and think, "I'll need this later," but you don't want to stop what you're doing. Then you're in Claude. Cursor. Chrome. Back to ChatGPT.

Before long, you've got little things scattered everywhere. An answer you want to keep. A link. An idea. Or three follow-up prompts before the current one has even finished generating.

I had this problem, so I built Copper.

Copper combines the useful parts of a to-do list, a clipboard, and a scratchpad. It's built specifically for AI-assisted work. It sits next to where you work and is always one shortcut away. It works with all your AI apps, terminals and browsers.

Whenever I find something worth keeping, I capture it. If I think of a few prompts while the AI is still responding, I type them into Copper. Then I send them back into ChatGPT, Claude, or Cursor and check them off as I go.

Copper is local and private. It doesn't sync anything, doesn't collect anything, and doesn't need an account.

I've used Copper every day for the past few months. It has completely changed how I work.

If this feels like something you'd use, it's $39. One-time purchase.

[Guillermo Rauch @rauchg]

https://t.co/uNXS9jEgpg is Next.js for agents.

I built Next with a simple premise: 𝚙𝚊𝚐𝚎𝚜/𝚒𝚗𝚍𝚎𝚡.𝚓𝚜 is all you need. Put some React in there and you’re good to go.

Eve asks for even less. 𝚊𝚐𝚎𝚗𝚝/𝚒𝚗𝚜𝚝𝚛𝚞𝚌𝚝𝚒𝚘𝚗𝚜.𝚖𝚍. Put some English in there and you’re good to go.

Like Next, it embraces the filesystem. You can guess what 𝚝𝚘𝚘𝚕𝚜/𝚚𝚞𝚎𝚛𝚢-𝚍𝚋.𝚝𝚜 does. An agent is just a directory, whose entire spec fits in the tweet below.

And like Next on Vercel, it’s seamless to deploy. The infra, like Sandbox, Gateway, Workflow… is the output of your creation.

[Karim Atiyeh @karimatiyeh]

Proud of this one. What started as an internal cost-saving hack now routes AI for 70,000 customers and today it's open to everyone.

The best model changes every week. Your code doesn't have to.

[Sebastian Buzdugan @sebuzdugan]

Most AI features ship with zero risk review

So I built a gate that refuses to let them:

npx frai-gate blocks your spec until 7 responsible AI checks are answered: risk tier, data, oversight, evals, bias, monitoring, transparency

Empty answers = CI fails