meejam

What's hot and new at

[Sam Altman @sama]

this would have been a whole startup not too long ago

[Aakash Gupta @aakashgupta]

Apple just sued OpenAI, and the wildest part is how they got caught: one candidate screenshotted confidential Apple files on his Apple work laptop hours before his OpenAI interview. Apple reads its own server logs. The recruiting pipeline generated its own evidence trail.

The complaint says OpenAI's hardware chief Tang Tan, a 24-year Apple veteran, directed candidates still employed at Apple to bring "actual parts" (batteries, logic boards) to interviews for show and tell sessions. One candidate was surprised, saying he didn't even know you could take those out of the office.

Apple also alleges Tan circulated an internal Apple offboarding document to coach new hires on dodging exit security checks, and that a departing engineer kept his Apple laptop, found a bug that still gave him access to Apple's cloud storage, and downloaded dozens of confidential hardware files after joining OpenAI.

Then the supplier: OpenAI allegedly got one of Apple's manufacturing partners to demonstrate a proprietary metal finishing technique by letting the partner believe Apple had approved it.

Over 400 former Apple employees now work at OpenAI. Apple says it flagged all of this to OpenAI in February and never got a response. Five months later, it filed.

The ask reveals the strategy. Apple wants an injunction barring OpenAI from using the secrets, the return of every file, and full discovery into io, right as OpenAI preps its first device launch and an IPO. If a judge grants it, OpenAI may have to prove the device was built clean, component by component, before it ships.

The device was supposed to run on the world's best hardware talent. Now its bill of materials is evidence.

[meejam @meejam]

@FbforReal08 @its_Jnra @nabunturanguy Tax for the Philippines ba yung tax na tinutukoy mo? Hindi diba? So what’s your point?

[meejam @meejam]

@_avichawla Check the internals of Antigravity 2.0. It’s almost everything you described and more.

[Granite @Granite0x]

He leads engineering on Gemini at Google.

instead of keeping his Claude setup private, he open-sourced it.

Addy Osmani. That Google: Chrome DevTools lead, "Learning JavaScript Design Patterns" author.

'agent-skills' - his personal loadout. Drop-in for any project.

68,925 stars. MIT.

→ https://t.co/n7NI3Sr7uu

bookmark it. This is how your Claude setup goes pro.

[meejam @meejam]

@cyrilXBT The gold is for Anthropic lol. Cut the bs. Human judgement cannot be replaced, at least before ASI and we let ASI decide for us.

[K2S @k2sbhai]

nvidia is casually giving you access to 5 frontier chinese AI models for free 😳

no credit card
no subscriptions
just one API key that unlocks everything

what you get for $0:

- DeepSeek V4 Flash for ultra-fast inference
- MiniMax M3 as a drop-in coding assistant
- Qwen3.5-397B for advanced reasoning tasks
- Kimi K2.6 for agentic workflows and long chains
- GLM 5.1 as a reliable everyday model

why this is huge:

> no paying separate subscriptions for different models
> no changing your existing workflows or tools
> no vendor lock-in since everything is OpenAI-compatible

getting started takes less than 2 minutes:

1. go to https://t.co/q50rSatNbb
2. sign up and verify your account
3. generate your nvapi key
4. set your base URL to https://t.co/92kkbFSf8D
5. pick any model and start building

supported models:

> minimaxai/minimax-m3
> qwen/qwen3.5-397b-a17b
> moonshotai/kimi-k2.6
> zhipuai/glm-5.1
> deepseek/deepseek-v4-flash

pro tip:

use DeepSeek V4 Flash for speed, Qwen for hard reasoning, Kimi for agents, and MiniMax as your daily coding companion

the best part?

one free key gives you access to 100+ models across NVIDIA's catalog

~40 requests per minute is more than enough for most developers and personal projects

5 frontier models that compete with GPT and Claude, all without spending a dollar

bookmark this and claim your free API key before the limits change 👀

[Anatoli Kopadze @AnatoliKopadze]

Anthropic engineer:

"You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day."

In 45 minutes he shows exactly how to do it from scratch, step by step.

Most people are still doing all of this by hand.

Watch the session, then save the guide below.

[Steve (Builder.io) @Steve8708]

When I'm working on code I'm not familiar with, I like to use the /visual-plan skill to visualize how the code works first.

Running a prompt like "/visual-plan show me how the user APIs work" it'll generate the docs I wish the repo had.

APIs are laid out in a Swagger-like UI with parameters, response types, etc.

Architecture and data flows are generated as diagrams, UX flows as storyboards, underlying data structures as schema maps, etc.

The most important part: the /visual-plan skill uses MDX under the hood, so it's surprisingly token efficient.

It doesn't code all of this UI from scratch each time like outputting HTML, it instead uses reusable components with JSX that you can customize with your own components.

There's also a GitHub action you can install that makes it so Claude or Codex can post a visual recap on every pull request, so you can easily scan exactly what changed in the same way. Visual, scannable diffs of the UIs, APIs, and the key code that updated with annotations.

This is all open source on my GitHub, will link to them in the thread:

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[meejam @meejam]

@bcherny I think you are describing the now or maybe the transition era before AGI. The future to me is just 1 and 4. These 2 involves human to human interactions.

[Boris Cherny @bcherny]

As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:

1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales

Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.

A healthy team needs a mix of these, depending on the product:

- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2

Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?

[Dan @KettlebellDan]

for someone who says he doesn’t care if AI is open source he seems to care a lot

[Matt Pocock @mattpocockuk]

Here's a way to get a visceral sense of the dumb zone (starts at ~120K tokens on SOTA models):

1. Agent does something dumb
2. /rewind to before the stupid thing
3. /compact (assuming you're in a spot where compacting makes sense)
4. Run it again

Watch it get smarter

[Dr Singularity @Dr_Singularity]

this seems big

Researchers realized a programmable probabilistic computer with 1,000,000 p-bits

[Charly Wargnier @DataChaz]

🚨 A SENIOR ANTHROPIC ENGINEER JUST DROPPED AN 11-PAGE PDF ON LOOP ENGINEERING.

The core shift: stop prompting the agent. Build the system that prompts it.

Inside the autonomous loop:

- Discover → Finds its own work (failing CI, open issues).
- Isolate → Uses separate git worktrees to prevent collisions.
- Verify → A second agent reviews the work. (Never let agents self-grade).
- Persist → Writes to disk, not temporary context windows.
- Schedule → Runs automatically on a timer.

This is a great framework for building more reliable agentic systems

link to the guide below.

Read it, then check out this ace article on Loop Engineering by @akshay_pachaar 👇

[Steve (Builder.io) @Steve8708]

Introducing the Clips chrome extension - the easiest way to send bug reports to agents with video, transcript, and browser debug info captured automatically.

100% free and open source.

If you are like me and get tired of manually typing instructions to agents, attaching screenshots, pasting debug logs, and all of that, this might be your new favorite tool.

With the Clips chrome extension, you can just click the Clips icon, hit record, and start talking.

Visually demonstrate your issue, go through the flow, point out what’s broken.

Clips will capture everything on your screen, plus network requests, browser logs, client errors, and all the details around them. And it redacts sensitive information.

Then it gives you a link you can send to humans so they can play it and take a look. Or, more importantly, just give it to your agents by just pasting the URL to them.

The link has special metadata for agents so just from the URL, the agent can pull all information from the clip automatically. No plugin or MCP server required.

That means it can "see and hear" what’s in the video - read the transcript, grab snapshots at any timestamp, and inspect the logs and network requests that were shared with it.

So whether you want to quickly demo an issue and send all that context to an agent, or get better bug reports from teammates, recording and sending Clips makes that super easy.

Unlike expensive apps like Loom, this is all 100% free and open source.

The framework that powers this, plus a bunch of other free applications, is open source too. You can just sign up and use it, or fork it and customize it to your needs.

This, in my opinion, is the future of software.

Rather than bloated SaaS that charges you a ton of money and still doesn’t even have the things you need, we get free open source canonical apps that you can fork and customize in any way you want.

I'll link to all this stuff in the replies.

If you try it, let me know your feedback.

[Charly Wargnier @DataChaz]

If you spend your day in the terminal, your core infrastructure just got an upgrade.

🚨 A new open-source project called RMUX is about to make your command line INSANELY more powerful.

Sure, it’s a new Rust-based terminal multiplexer, but calling it a "tmux clone" completely misses the point.

Instead of just splitting windows, RMUX runs a local daemon that natively manages your entire PTY and process lifecycle.

Here is what makes it a shift:
→ Drives exactly like tmux (90+ native commands)
→ Built from the ground up in Rust
→ Fully programmable via official Python, TypeScript, and Rust SDKs
→ Runs natively on macOS, Linux, and Windows (no WSL required)

The wildest feature?

You can securely share a live terminal pane directly to a browser!

It uses a decoupled static UI protected by post-quantum encryption 💥💥💥

Best of all: it is 100% free and open-source (MIT/Apache-2.0).

Here is a breakdown of why this matters 🧵↓

[Prajwal Tomar @PrajwalTomar_]

Vibe coders are getting sued.

People are shipping apps with real users and skipping the boring stuff that kills them.

A 20+ year dev shared the pre-launch checklist every AI builder needs.

I added what I learned after shipping 60+ apps at the agency.

Don't skip this:

1. Protect yourself, not just your app. The moment you collect user data you're in legal territory (GDPR, CCPA). Have a privacy policy. Know where user data lives.

2. Row Level Security. Without RLS, anyone can open DevTools and read your entire database. Supabase → Auth → Policies. Zero policies means your app is naked. 5 min to fix.

3. Test the failure path, not just the happy path. Wrong password 5x. Reset for an email that doesn't exist. Verification link clicked twice. Signup with an existing email. Catches 80% of auth bugs.

4. Security baseline in 2 min. Prompt your AI: "Review my app as a security specialist and make sure I have strong security headers and a solid baseline security posture."

5. OWASP. Prompt: "Review my app against OWASP standards and highlight vulnerabilities." This is where SQL injection, XSS and auth bugs actually get caught.

6. Client-side validation is UX, not security. Attackers disable JS and hit your API directly. Validate again on the server. Every time.

7. AI code leaks data in 3 spots: .env values in the frontend, API responses returning too much, secrets in logs. Prompt: "Check my app for credential or sensitive data leaks in frontend or API routes."

8. API keys in the frontend means game over. If it's in the browser, assume it's already taken. Move it server-side or proxy it.

9. Rate limits before someone burns your API bill. Cap every endpoint hitting a paid API. I've watched a Supabase bill jump from $20 to $200 in a day.

10. CAPTCHA on public forms (Cloudflare Turnstile is free) plus CORS locked to your domain. 10 min, kills bot floods.

11. Error messages that don't leak. "User not found", not "SELECT * FROM users failed". Log full errors server-side, show users generic messages.

Build fast. Just don't ship naked.

(full breakdown in my article below)

[Jami @expertwith_AI]

My girlfriend asked why I was smiling at my phone at 3AM.

I lost my job last week.

Rent due in 4 days.

No backup plan.

Then I found a 33-year-old nerd who turned $1,000 into $946,207 trading Bitcoin with a trick he stole from hurricane forecasts.

No finance degree. No trading desk. Just a method every meteorologist uses and every trader ignores.

The method: meteorologists never forecast tomorrow with a single model. They run 31 and count the votes. He applied that exact framework to Bitcoin.

Built a Claude agent that reads every 5-minute BTC candle and feeds it into MiroFish simulator running 31 parallel prediction paths.

Trade only fires when 28 out of 31 models agree.

Below 26 votes? Trade dies instantly.

The agent moves faster than any human trading desk:

→ Collects market data 24/7 without breaks
→ Runs continuous simulations inside MiroFish engine
→ Operates fully autonomous with zero manual input
→ Every trade executes only when consensus hits threshold
→ Every dollar captured is pure market inefficiency exploit

That is the entire edge.

Not prediction. Consensus.

Position sizing follows Kelly criterion. Signal fires or it does not. Most signals fail the vote count, so the system stays flat most days.

He spent years learning that certainty is a scam and consensus is the only edge that matters.

You only need Claude + device + 1 hour per day.

Giving this free for 24 hours.

To get it:

1. Comment the word Claude
2. Like and retweet this
3. Follow me @expertwith_AI so I can DM you

Save this post. Build the consensus system this week. Start with $200. Scale on evidence.