Red Pill

[FHILY👑 @Oluwaphilemon1]

Claude Code creator:

"100% of our pull requests at Anrtopic are run by Claude Code. 80–90% of code review too.

The feature I’m using the most today is /loops. I’m not prompting Claude anymore - I’m building loops"

in 1-hour interview, Boris reveals his setup, which helps him build the #1 coding tool of this year.

Worth more than a $500 vibe-coding course.

[0xMarioNawfal @RoundtableSpace]

Anthropic shipped a CLI for Claude Platform every API endpoint now runs straight from your terminal.

Your terminal just became a direct line into Claude's infrastructure.

[Charly Wargnier @DataChaz]

MANUALLY DRAGGING BOXES FOR ARCHITECTURE DIAGRAMS IS FINALLY DEAD

There is a new open-source agent skill that turns raw codebases into cleanly routed https://t.co/f9zjBxfJLp diagrams without you placing a single coordinate.

The project, drawio-skill, runs directly inside Claude Code, Cursor, or Copilot.

Instead of opening a blank canvas, you just ask your agent to map the repo.

Here is what it actually does:
→ Extracts the module structure (supports Python, JS/TS, Go, Rust)
→ Uses Graphviz for auto-layout and routing
→ Drops redundant edges so the graph stays readable
→ Builds native, editable https://t.co/f9zjBxfJLp files

But the standout feature is visual self-checking.

Once it generates the diagram, the agent "looks" at the resulting PNG. If it sees stacked edges or clipped text, it auto-fixes the layout across up to 5 iterative rounds.

It runs from a single file. No MCP server. No background daemon.

Best part?

It's 100% free and open-source.

repo link in 🧵↓

[digital ghost @vibeeval]

geçen hafta claude ile 6 saat dijital ayak izimi sildim.

sonuç: 47 data broker listingi kaldırıldı. 12 ölü hesap silindi. 3 arama sonucu bastırıldı.

nasıl yaptım, adım adım. thread.

[Michael Guo @Michaelzsguo]

Hackers can use a crafted GGUF file to leak private information you put into your local LLM or agent.

Many people may not have a good understanding of what GGUF is, so here is a simple primer.

GGUF is easier to understand if you stop thinking of it as “a model” and start thinking of it as a container file.

A local LLM is mostly a giant pile of numbers: the weights. But the runtime (llama.cpp for example) also needs a map:

what architecture is this?
what tokenizer should be used?
what chat template applies?
where does each tensor start?
what shape is it?
what quantization type is it?
GGUF packs that into one file.

The most important part is the tensor table. It is basically a manifest that says:

tensor: blk.0.attn_q.weight
shape: [4096, 4096]
type: Q4_K_M
offset: 1835008

The loader reads that and says: “go to this byte position, interpret these bytes as this kind of tensor, and attach it to this layer.”

That’s why GGUF is so useful for local inference: one portable file can include weights + metadata + tokenizer info + quantization layout.

And that’s also why GGUF parsing matters for security. A GGUF file is structured input to a model loader. If the file lies about offsets, sizes, or shapes, the loader has to catch it.

GGUF is normally associated with llama.cpp and most commonly stands for GGML Universal Format. The “GG” comes from the initials of llama.cpp creator Georgi Gerganov. Some people may also call it GPT-Generated Unified Format.

Ollama uses llama.cpp under the hood. Many tools support GGUF because they wrap or interoperate with llama.cpp. Hugging Face Transformers can load GGUF for specialized workflows, and some runtimes/tools expose GGUF import/export. MLX has GGUF-related save/export utilities, but MLX models usually use MLX-native weights, not GGUF as the main runtime format.

[DeepTechTR 🇹🇷 @DeepTechTR]

Çin, 7/24 kendi kendine çalışan ve %100 yerel olarak çalışan bir yapay zekâ çalışanı piyasaya sürdü.

Araştırma yapıyor, kod yazıyor, web siteleri oluşturuyor, slayt sunumları hazırlıyor ve videolar üretiyor. Hepsi kendi başına. Hepsi bilgisayarınızda.

%100 Açık Kaynak.

[Higgsfield AI 🧩 @higgsfield]

Higgsfield releases Virality Predictor

What does it mean:
> Upload any clip up to 15s
> Get viral potential, hook score & hold rate
> See a heatmap of brain regions your clip activates
> Pair with Ad Reference for recreated videos

Available via MCP/CLI and on the platform.

[AYi @AYi_AInotes]

Damn，刷到这个帖子我瞬间愣了，AI迭代速度已经疯了，有点跟不上节奏了🤯

我一开始以为这又是一个生成视频的新工具，点进去发现根本不是一回事。

Higgsfield昨天刚发的Virality Predictor，直接把脑科学塞进了内容创作流程。

上传一段15秒以内的视频，
几秒就能给你出完整的人脑反应报告。

整体病毒潜力，
前3秒钩子得分，
全程注意力保持曲线，
还有彩色的脑区激活热图，
告诉你观众的大脑哪部分��点亮了。

最狠的是它和Claude的打通，
可以一句话生成500个产品视频变体，
然后全部丢进预测器自动筛掉垃圾，
只放大高分版本直接投流，
从0到已验证高潜力广告，全程自动化。

它不是看你视频过去像不像爆款。
而是用AI模拟700多个人、500多小时fMRI数据训练出来的人脑，预测你看到这段视频时的真实神经反应。

我们以前做电商短视频，
拍，剪，烧钱测，看数据，迭代，
测试成本是真金白银的广告费。

现在做电商短视频，
生成，预测，只投高分，
测试成本只是一点token费。

最牛逼的地方在于
大脑亮不等于一定爆，
一些高认知负荷的内容会让大脑很活跃，但普通人刷到就会直接滑走。

真正的病毒性，只看前3秒钩子得分和视觉拉力。
和你有没有深度，有没有道理，一点关系都没有。

也就是说以前爆款靠灵感，
现在爆款靠系统。

所以当别人还在熬夜想创意的时候，
你已经用AI筛完了500个版本，
知道哪10个能爆。

而且工具越强，越考验人的判断，
它能帮你砍掉80%的无效创意，
但选什么方向，解读什么结果，
永远是人说了算。

我感觉2026年的电商短视频战场，
已经不是比谁创意好，
而是比谁能用AI先把非爆款干掉，

所有还在靠感觉做内容的人可真的要跟不上了。

#AI工具 #短视频创业

[elvis @omarsar0]

// Scalable Patterns for Agentic AI Workflows //

Besides context engineering, we should be putting a lot more system engineering efforts around agents.

This paper shows an example of why it matters.

(bookmark it)

Let's start with an important question: Where does your agentic RAG pipeline actually lose time?

It's almost never the LLM call. It's usually the data plane underneath. Serialization between preprocessing, embedding, and vector retrieval, plus coordination overhead between distributed services.

New work introduces AAFLOW, a unified distributed runtime that models agentic workflows as an operator abstraction over Apache Arrow and Cylon. A zero-copy data plane connects preprocessing, embedding, and retrieval directly. Resource-deterministic scheduling and async batching cut coordination cost.

The result: up to 4.64× pipeline speedup and 2.8× gains in embedding and upsert phases, with comparable LLM throughput.

None of that comes from LLM inference acceleration. It all comes from cleaner data flow.

Paper: https://t.co/9fqkRRsV39

Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

[Anatoli Kopadze @AnatoliKopadze]

THIS GUY JUST BUILT A DRONE THAT TRACKS TARGETS WITH A LASER USING CLAUDE

> No robotics team
> No engineering degree
> No budget

He used Claude Code to write the entire system in Python - a drone that locks onto a target, follows it with a laser and corrects its own aim automatically after every attempt.

The AI gets more accurate the longer it runs because it learns from every single shot.

He's still improving it and this is only the beginning.

You have the same tool he used.

The article below is about how to actually use it to its full potential.

[Poonam Soni @CodeByPoonam]

Claude Opus 4.7 vs Kimi K2.6

It's not even close.

3 months ago nobody believed open-source could beat Claude. Today it did.

Full guide with Prompt 👇

[Tom Dörr @tom_doerr]

Interactive visualization of Transformer internals

https://t.co/BxUuXBt29N

[How To Prompt @HowToPrompt__]

Someone just built a tool that turns any GitHub repo into an interactive knowledge graph.

You just paste any repo, and it builds a live D3.js graph of every function, class, and call relationship, then lets you query it in plain English with an AI agent.

100% Open Source and runs in your browser.

[Dilum Sanjaya @DilumSanjaya]

Fun interactive science app ideas | Part 3

Played around with generating 3D biological structures and made an app to explore them interactively

UI Design
GPT Images 2

Code
Gemini 3.1 Pro

More demos ↓

[Umesh @umesh_ai]

The AI for floor plans!

You can now draw a custom shape and turn it into a cool floor plan.

@DraftedAI makes it all look easy.

[Thariq @trq212]

https://t.co/MXt5XS4xBX

[WorldofAI @intheworldofai]

There’s now a Hermes Agent Desktop App

You can finally run the self-evolving 24/7 autonomous AI agent from Nous Research inside a full native desktop UI instead of only the CLI.

Multi-agent management, persistent memory, autonomous workflows, and local infrastructure support all inside one app. Full demo: https://t.co/qbLRPjJd2y

[0xMarioNawfal @RoundtableSpace]

Google just launched CodeWiki paste any repo & it auto-generates the entire documentation.

> Diagrams, architecture maps, dependency trees
> Step-by-step tutorials built automatically
> A chatbot trained on your whole codebase, ready to answer questions

[dunik @dunik_7]

40% of the code Claude writes for you is wasted. you're paying for the rewrite.

a 65-line markdown file fixes it. 120,000 developers have starred it.

the author tested it on "30 codebases over 6 weeks" and reported a mistake rate drop from 41% to either 11% or 3%

depending on whether you read the headline or the body.

the irony is that the article is right.

CLAUDE.md is the most under-leveraged file in your stack.

65 lines of behavioral rules outperform a 4,000-token preferences dump.

"be careful" is useless. testable imperatives are gold.

"be senior" doesn't work Claude already thinks it is.

the 4 rules that ship the most leverage:

/ state assumptions, never guess silently
/ minimum code, nothing speculative
/ surgical changes, don't refactor adjacent code
/ define success, loop until verified

compliance: ~80%. mistake rate: from ~40% to single digits.

no human caught the contradicting numbers in the title.

nobody had to.