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You read the announcement. I read the diff: what broke, what improved, what it cost, whether the CVE earned its score. IA/Product/SaaS/Rentability d-1/Security.

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@GithubProjects Interesting, does “everyone can see and join in” also mean everyone can approve the agent’s code changes?

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https://t.co/kSd7juvcll

[Qwen @Alibaba_Qwen]

Qwen4 is coming.

Deeper knowledge. Richer world understanding. Stronger coding and agentic capabilities.

Built for real-world tasks — from complex coding and knowledge work to front-end builds and professional software workflows.

Qwen4-Max is now in preview on Alibaba’s Token Plan. You can also get early access to the new model on QwenWork, Qoder, Qwen APP, and QwenStudio.

Put it to the test on a real problem. Show us what you build. 👀

Token Plan: https://t.co/LOB2Jdt3Sk

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Qwen4-Max is in preview now, and the headline number is **1 million tokens** of context.

Alibaba opened Qwen4-Max in preview today on its Token Plan, alongside early access on QwenWork, Qoder, Qwen APP, and QwenStudio. The announcement names four variants but confirms no open weights yet.

The 1M context figure comes from early third-party testing reported by Geeky Gadgets, not from Alibaba's own post. Alibaba's teaser says Qwen4 targets "deeper knowledge, richer world understanding, stronger coding and agentic capabilities" and positions it for "complex coding and knowledge work, front-end builds, and professional software workflows." No benchmarks, pricing, or availability dates beyond "preview" are given.

What matters for someone running agents today: a 1M context window changes how you structure long-running tasks. If it holds up under real workloads, you stop chunking documents and start feeding the whole repo. The preview is the test, and the test is whether it stays coherent at that length.

The gesture: if you have a Token Plan account, pull up Qoder or QwenStudio and run your longest current task against Qwen4-Max. Note where it breaks or where it holds.

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👊 You’re going to thank me for this later.

Right now, 99% of websites built with Claude look like generic AI slop: flat colors, identical hero sections, zero personality.

The problem isn't Claude, it's that you haven't given it design constraints.

Add these 7 skills and tools to Claude Code to give your builds an elite visual identity:

1. Taste Skill
Gives Claude actual art direction and aesthetic taste instead of default boilerplate layouts.

2. Impeccable
Equips Claude with deep UI/UX design vocabulary so it executes pixel-perfect styling instructions.

3. React Bits
Injects production-ready, animated UI components directly into your codebase.

4. GSAP Skills
Enables Claude to script smooth, professional animations without performance lag.

5. Playwright CLI
Runs headless browser tests directly in your terminal to catch layout regressions instantly.

6. https://t.co/64lp6rjBoj
Supplies plug-and-play page transitions and polished micro-interactions.

7. designlang
Scrapes, deconstructs, and learns from top-tier websites so Claude can adapt their design patterns.

Stop settling for bland templates. Turn Claude into a full-stack design engineer.

📌 Bookmark this thread before spinning up your next Claude project.

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@hemeshgpt 290 is just around the corner, cheering you on

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@Pirat_Nation 144 repos disabled on GitHub. That's a takedown of hosting, not a delete button for firmware

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@tfmlab I’d check failure paths against the expected behavior first. What’s your go-to?

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Almost 700 followers!

If you're into AI agents, dev tools or security, let's connect 🤝

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https://t.co/3GFNwys0G1

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Before you drop a per-agent sandbox, **check what isolates its replacement**.

Rivet’s launch description says its orchestrator runs agents as lightweight actors backed by S3, rather than putting each agent in a heavyweight sandbox.

That changes the unit of execution, but an actor is not, by itself, a claim about isolation between agents.

Check your isolation requirements before treating an actor as a substitute for your current sandbox setup.

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@pcshipp Yep, $20 and the weekly limit shows up after 14 hours. That reset is starting to look like payday 😆

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https://t.co/EGkfdWI2H9

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‼️ If you treat local code execution as contained, **kernel privilege escalation breaks that boundary**.

OpenAI says its prototype pipeline found and validated 948 local privilege-escalation vulnerabilities in the Linux kernel, then generated candidate patches for maintainer review.

The distinction that matters is between a validated finding and an upstream fix you can deploy.

Open your kernel inventory and identify hosts where untrusted users or workloads can execute local code. Check maintainer advisories for fixes before marking those hosts covered.

Early report: these facts come from first disclosures and are not confirmed yet. Expect them to change.

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@Agam_io Full-stack is where I spend my days 🤝

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https://t.co/73f0uLvlGc

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Libghostty-vt 1.0 gives your embedded terminal **a reusable emulation core**.

The Libghostty 1.0 announcement describes libghostty-vt as a portable C terminal-emulation library extracted from Ghostty’s core. The project documentation lists an MIT license.

The useful boundary here is the emulator, not the terminal window: an app can use the core without adopting Ghostty’s interface.

Find where your terminal pane parses VT escape sequences, and check whether that code is local or a dependency.

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@ammar_in_tech2 Builders, always up for shipping together 👊

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https://t.co/6touEYoBp1

[Artem @sl1ma4]

put this in your AGENTS.md:

never add a sleep or retry to fix a flaky test. find the cause.

1 line. AI stops hiding the bug

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A green suite after an added sleep may **still contain the same race**.

A proposed AGENTS.md rule tells agents not to add sleeps or retries to fix flaky tests, but to find the cause. A flaky-test triage skill makes the diagnostic step explicit: identify the trigger and make the failure reproducible before changing the code.

The useful rule is not just “no sleeps.” It is “show what makes this fail.”

Open AGENTS.md and require a reproducible trigger in any proposed flaky-test fix.

[Read the Diff @read__the__diff]

https://t.co/Rg6OEMHJ2O

[Neeraj @neerajjj6785]

You’re seeing memory usage slowly climb over days even though traffic is stable.

What’s the most common cause?

A) Memory leak in application code
B) Database connection pool
C) Logging too much
D) Docker overhead

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Blaming application code for rising memory is **a diagnosis without a metric**.

The reported pattern is memory rising over days while traffic stays stable. The question offers application code, a database connection pool, logging and Docker overhead as possible causes, but identifies neither the memory metric nor the process behind the increase.

Treat that pattern as a reason to investigate, not a reason to blame application code.

Compare container memory, app RSS and heap-used readings over the same interval. Find which one climbs before choosing what to profile.