Pramod Rudrapatna

Internet & Tech enthusiast, Market economics believer, Bullish about AI. Tweets are mine.

[Pramod Rudrapatna @PramodDikshith]

@AdaFang_ are you planning to open more seats. looks like its already filled.

[Nav Toor @heynavtoor]

A scientist in Denmark figured out how to make Claude prepare his job applications. He open-sourced the whole thing.

His name is Mads Lorentzen. He is a PhD geophysicist. He built it on top of Claude Code and released it under MIT license.

Here is what it does. You fork the repo, fill in your background once, and it runs a five-step pipeline for every job you want to apply to.

Step 1. It reads the job posting and scores how well you fit.

Step 2. It drafts a tailored CV in LaTeX, picking only the experience that matches.

Step 3. It writes a cover letter framed around what you would bring to the role.

Step 4. A second AI agent reviews the first agent's work, points out weaknesses, and the first agent revises.

Step 5. It compiles both into clean PDFs you can send.

The whole thing is a folder of markdown files. The candidate profile, the writing style rules, the CV templates, the interview prep notes. Every step is plain text you can read and change.

The job portal search is built for Danish boards. The application workflow itself works for any country.

489 stars. 270 forks. A fork-to-star ratio that high means people are using it, not only bookmarking.

Mads is not a startup founder. He built this because he needed it for himself, then shared it.

This is the future of job hunting. Not a service you pay for. A workflow you own.

(Link in the comments)

[David Ball @davieball]

For those waiting for the right time to explore their genome, this is it.

30-100x Whole Genome Sequencing, with annotation, in a format built for AI-assisted self exploration.

Works with Codex, Claude Code, Cursor and more.

[Garry Tan @garrytan]

We're still writing code like it's 2013: no latent space, no intelligence. When there is intelligence we gate the agents like workers at a Foxconn factory.

The future of software is just-in-time and is 10x less code because of the markdown.

And the agents will be free.

[Thariq @trq212]

been asking others at Anthropic how they stay in the loop with Claude and fully understand the work being done

this is one of my favorites from Suzanne:

[Khairallah AL-Awady @eng_khairallah1]

Anthropic engineer:

"You're not supposed to prompt Claude. You're supposed to build a system that prompts itself."

this is one of the best workflows I've seen in a long time

in this video she breaks down exactly how most people are using Claude:

- the 14% you lose to CLAUDE.md before typing a word
- the plugins that 95% of users have never installed
- the workflows that run without you typing a single prompt
- why typing one prompt and closing the tab is leaving 90% on the table

if you've been using Claude for months and still start every session from scratch, you have at least 28 untouched features. probably 30

instead of another show tonight, watch this

make sure to bookmark it before it gets lost in your feed

full guide in the article below

[恒星 @vintcessun]

一早刷到个有意思的东西：DeepMind 把 AlphaGenome、UniProt 等 30+ 科学数据库打包成了 agent 技能。

科学类 agent 最大的问题不是模型不够好，而是不知道怎么正确调用数据库。幻觉高、token 浪费严重。

本质上，skills 把每个数据库的 API 交互拆成明确指令 + 脚本，agent 按步骤执行而不是靠猜。安装一行 npx，还能直接挂到 Antigravity 里用。

https://t.co/ESrP1e3TQI

[Philipp Schmid @_philschmid]

Gemini Managed Agents Dev Guide: 1 API call = Gemini 3.5 Flash + Antigravity Harness + remote Linux sandbox. No infra, no orchestration.

- Antigravity quickstart (code/files/browsing)
- Persistent multi-turn + streaming
- Custom agents (AGENTS.md + mounts)
- Ops: snapshots, allowlists, egress creds

[Pushmeet Kohli @pushmeet]

When I was asked by the American Academy of Arts and Sciences to write an essay on my thoughts on how AI will accelerate Science, I felt honored but also felt that it would require a lot of thoughtfulness and diligence to distill my thoughts on paper.

The essay has now been published and I cannot be more thankful to the @americanacad and @GoogleDeepMind teams for their feedback and encouragement during the process.

Key reflections from my essay:
🔭 AI is our newest revolutionary lens: Just as the telescope and microscope expanded our physical perception, AI is extending our cognitive reach, allowing us to decipher the immense complexity of the data-universe.
🧬 The rise of "machine intuition": AI is not just a computational engine. By detecting hidden structures across disciplines—from protein folding to extremal combinatorics—it acts as an ultimate bridge, accelerating the interdisciplinary breakthroughs that modern science depends on.
🏗️ From puzzle-solvers to architects of questions: As we transition toward open-ended, agentic AI systems that actively generate novel hypotheses, the burden of reasoning is shifting. We are evolving from being the solvers of intricate puzzles into the architects of profound scientific questions.
✨ Expanding human potential: AI won't replace scientists; it expands what we can imagine and achieve. Just as the telescope didn't make astronomers obsolete, AI is giving us the stars.

Read the full essay here: https://t.co/LCoF7ds7WZ

[Replica @John82924749]

🚨🚨Shunyu Yao is currently a Senior Staff Research Scientist at DeepMind. This interview was recorded around May 10 and runs nearly four hours in full. I selected the parts that I personally found most interesting, covering the following topics:

[Huaxiu Yao @HuaxiuYaoML]

Every memory system for LLM agents evolves what it stores. None evolves how it retrieves.

🧬 EvolveMem is out, now shipping inside the SimpleMem v0.3.0 update. Powered by AutoResearch: the system researches its own retrieval, treating the full retrieval config as a structured action space and running a closed loop: evaluate ➜ diagnose ➜ propose ➜ validate ➜ repeat.

🔬 From a minimal baseline, 7 autonomous rounds produce a retrieval policy that beats the strongest published baseline by +25.7% on LoCoMo and +18.9% on MemBench.

🧬 It discovers entirely new retrieval dimensions not present in the original design, all integrated into the unified SimpleMem package.

📄 Paper: https://t.co/BWCXebWhG1
💻 Code: https://t.co/hhdgvVjblP

Led by @itsJiaqiLiu, @XinyeYee with contributions from @richardxp888, @ZhengBerkeley, @cihangxie

[Shubham Saboo @Saboo_Shubham_]

This is HOW my Hermes and OpenClaw Agents share a single brain.

Hermes Agent is the orchestrator with OpenClaw agents as the squad.

[Wei-Hung Weng @ckbjimmy]

Science Skills from @GoogleDeepMind are now live in Google @antigravity! Explore our new agentic science workbench: https://t.co/tBEPsqNLgw

[Pushmeet Kohli @pushmeet]

The results of the research happening in my team @GoogleDeepMind have convinced me that the next era of scientific discovery will be aided by AI agents acting as force multipliers for human ingenuity.

That’s why I’m proud to introduce Gemini for Science - a collection of experimental science tools designed to support researchers at every stage of the research process. The tools include:

1️⃣ Literature Insights, built with Google NotebookLM, searches millions of scientific papers to synthesize findings and generate artifacts including data tables, slides, reports, and more.

2️⃣ Hypothesis Generation, built with Co-Scientist, simulates the scientific method via a multi-agent "idea tournament" to generate, debate, and rigorously evaluate research hypotheses.

3️⃣Computational Discovery, built with AlphaEvolve and ERA, is an agentic engine that generates and scores thousands of code variations in parallel, allowing researchers to test modeling approaches in fields like epidemiology in a fraction of the usual time.

Read more: https://t.co/l8XIg8iXCN

Register for access here: https://t.co/V3YS15mRUS

[Garry Tan @garrytan]

Launching GBrain v0.12 with a self-wiring knowledge graph is now available

5% better precision
11% better recall
28% better graph search
53% fewer noisy results

Your markdown knowledge repo for OpenClaw and Hermes Agent just got even smarter

[Leonard Rodman @RodmanAi]

Claude isn’t a chatbot anymore.

It’s an AI engineering system.

And this repo proves it.

19.7K⭐
#1 trending on GitHub

claude-code-best-practice just changed the game:

→ 84 real tactics from Anthropic insiders
→ Actual agent architecture (not fake demos)
→ Subagents, MCP, hooks — fully implemented

But this is where it gets insane:

• Command → Agent → Skill orchestration
• Parallel agent teams (tmux + git worktrees)
• “Ralph Wiggum loop” = autonomous execution
• Claude + Codex reviewing each other 🤯

Most people will miss this part:

→ Plan with Opus
→ Execute with Sonnet
→ Spawn subagents instead of compressing context
→ Agentic search > RAG

If you’re still using Claude like a terminal chatbot…

you’re playing in 1x mode.

This is 10x.

100% open-source.

Link 👇
https://t.co/tTlYtK0QGz

[elvis @omarsar0]

// Self-Evolving Agent Protocol //

One of the more interesting papers I read this week.

(bookmark it if you are an AI dev)

The paper introduces Autogenesis, a self-evolving agent protocol where agents identify their own capability gaps, generate candidate improvements, validate them through testing, and integrate what works back into their own operational framework.

No retraining, no human patching, just an ongoing loop of assessment, proposal, validation, and integration.

Why it's worth reading this paper:

Static agents age quickly.

As deployment environments change and new tools arrive, the agents that survive will be the ones that can safely rewrite themselves. Autogenesis is part of a growing wave of self-improving agent systems, alongside work like Meta-Harness and the Darwin Gödel Machine line, and it's one of the cleaner protocol-level takes on continual self-improvement so far.

Paper: https://t.co/3aj9LLjSbk

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

[Movez @0xMovez]

This 30-minute speech by the Head of Anthropic "Coding Agents" researcher will teach you more about vibe coding than 100 paid courses.

Bookmark it & give it 30 minutes today. This video will change the way you use AI forever,

[Shubham Saboo @Saboo_Shubham_]

You can now run Qwen 3.5 397B parameter model on your MacBook.

48GB RAM. Pure C. Hand-tuned Metal shaders. No Python, no frameworks. 4.4 tok/s.

Built in 24 hours. Human + AI Agent pair programming. 90+ experiments.

[Pramod Rudrapatna @PramodDikshith]

@PrajwalTomar_ @Lovable DECK