Shubham

Exploring intelligence... human, artificial, and everything in between.

[Andrew Ng @AndrewYNg]

Good move by @JensenHuang. The Nvidia letter is well written and worth reading. As we saw with the OpenAI-Hugging Face hack, we need open models and harnesses for defense.

Lets stop believing the PR that closed models are safer. - that's just regulatory capture.

[Jensen Huang @JensenHuang]

Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community.

During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion.

That’s why we created the Open Secure AI Alliance.

[ali @waterloo_intern]

https://t.co/aivc7B4A7Z

[Gitanjali J Angmo @GitanjaliAngmo]

When @Wangchuk66 was detained, the BJP IT cell called us anti-national, Chinese agents, foreign-funded, and every other label they could manufacture.
Today, because we called the Congress' protest at PM's residence insincere, Congress trolls are calling us Sanghis, RSS and BJP stooges.
Different flags. Same playbook. The rot has infected our entire political culture.
India needs more than just a change of government. It needs a change in the very grammar of politics, where disagreement is met with dialogue, not demonisation; where patriotism is measured by integrity, not party affiliation; and where truth matters more than tribal loyalty.
@narendramodi @RahulGandhi

[Elon Musk @elonmusk]

It’s time.

[Andrew Ng @AndrewYNg]

Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry.

Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential.

OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose.

@rohitcprasad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think!

Try it out: https://t.co/P0mGnI1o31 (requires your own API key)
Source code: https://t.co/NYCiTD6hSq

[Salman Khan @BeingSalmanKhan]

It was such a peaceful movement, feel so sad that it had to take a violent turn. My heart goes out to the students and their families who were hurt. Paper leak is a very serious issue, and I am glad to know n see that the kids of our country have come together for a better educational system, and their parents supported them.

I truly appreciate the stand they have taken, showing dedication, sincerity, and keenness to study hard n make their future and a greater, educated India. This andolan, this protest, is the right way to go abt it, n the students have done this peacefully. So courageous and brave. Driven and motivated towards education, this generation will make India proud.

This issue is between the students and the educational system, it should not be hijacked politically, the credit should only go to the students of our country, and I am sure the government will also give them all the support n make it a stronger educational system. It’s a win-win situation. Hoping n praying for a positive decision. God bless all of you who wanna be educated.

Education should be the next trend and fashion, and should get trendier n more fashionable yr by yr, itna k bahar se log come to India to study and India becomes an educational hub.

[Rahul Gandhi @RahulGandhi]

प्रधानमंत्री मोदी भारत के इतिहास के सबसे युवा-विरोधी प्रधानमंत्री हैं - इतने युवा विरोधी कि एक नाकाम शिक्षा मंत्री धर्मेंद्र प्रधान का इस्तीफा भी नहीं ले सकते।

152 पेपर लीक। 7.5 करोड़ छात्र पीड़ित। और सज़ा एक दोषी को भी नहीं। सजा किसे मिली? मेहनती युवाओं को।

और जब इन बच्चों ने शिक्षा के जायज़ सवाल उठाए - तो जवाब में मिली लाठी और हिरासत। लीक करने वाले अपराधी आज़ाद - और वाजिब मुद्दे उठाने वाले छात्र घसीटे जाते हैं, पीटे जाते हैं।

यह सरकार सिर्फ़ युवाओं को नाकाम नहीं कर रही - उनपर टूट पड़ी है।

[Sonam Wangchuk @Wangchuk66]

I’m Not in good shape but not so bad either...
Rather than asking me to break my fast please join me on 20th July... Peaceful March to the Parliament.

#cockroachjanataparty #jantarmantar #cjpprotest #chalosansad

[Sundar Pichai @sundarpichai]

Well said Demis! Worth reading

[Mustafa @oprydai]

if you’re learning:

• robotics
• actuators, motors, gears, transmissions
• sensors, IMUs, encoders, cameras, LiDAR
• kinematics (forward, inverse)
• dynamics & control (PID, MPC)
• ROS2, rviz2, gazebo
• motion planning (RRT*, A*, trajectory optimization)
• SLAM (lidar, visual, fusion)
• drones
• autonomy stacks (perception → planning → control)
• embedded systems & microcontrollers
• power systems & batteries
• CAD & mechanical design
• supply chain and Manufacturing
• product design and development
• full stack engineering (software for hardware)
• systems thinking

welcome. this account is for you.

[rari @0xwhrrari]

Andrej Karpathy recorded 70 minutes

Breaking down how top AI users actually work with LLMs

And most people are making it way too complicated

Worth more than most $300 AI courses

Bookmark and watch it later

[precis0x @precisox]

Ex google engineer acaba de soltar un curso completo de 1 hora para construir agentes de IA que se mejoran solos, desde cero:

00:00 – Cómo nace un agente que se construye a sí mismo
03:01 – soul.md: el archivo que lo controla todo
30:16 – RAG inteligente: solo traes 20 mensajes relevantes, no los 2.000
31:48 – El loop que sabe cuándo parar solo
35:14 – Detectar el error y arreglar el prompt en el momento
50:22 – Cómo Claude comprime y optimiza tu memoria automáticamente

1 hora de contenido práctico que vale más que la mayoría de cursos de pago sobre agentes.

Míralo completo, guárdalo📚

[Codez @0xCodez]

Andrew Ng just released a 2-hour course on building agentic skills from scratch with Anthropic:

• 00:00 – How to build agent skills with Claude
• 22:32 – Claude pre-built skills for AI agents
• 41:07 – Agentic skills vs tools, MCP, subagents
• 01:06:06 – Skills for long-running agents

This 2-hour watch will replace 10 paid courses on building agents, by Anthropic themselves.

Watch it today, then read how to build self-improving agentic systems in the article below.

[Rahul @sairahul1]

Google just dropped a 1-hour course on agentic engineering from scratch:

00:00 – How to build your first AI agent
08:24 – Build agent memory (short, persistent, long)
28:34 – Agentic loops, long-running AI agents
40:04 – How to build MCP (MCP vs API)
1:00:22 – Multi-agentic systems

This 1-hour watch will replace 10 paid agentic courses on the internet.

Bookmark this. Watch this weekend.

[Jaydeep @_jaydeepkarale]

Great engineers learn fundamentals first.

• Semantic Search before RAG
• Embeddings before Vector Databases
• Learn Prompting limits before AI Agents
• Learn HTTP before REST APIs
• Learn SQL before ORMs
• Indexes before Query Optimization
• Learn Concurrency before Async frameworks
• Learn Networking basics before Kubernetes
• Learn Linux before Docker
• Learn CI fundamentals before GitHub Actions / GitLab CI
• Learn Monitoring concepts before Prometheus/Grafana
• Learn TLS before Service Mesh
• Learn Distributed systems basics before Microservices
• Learn Load balancing before API Gateways
• Learn Data modeling before NoSQL databases

[Praveen Kumar Verma @Alacritic_Super]

As an AI Infrastructure Engineer

Please learn:
- GPU architecture, VRAM fundamentals, CUDA & memory hierarchy
- Quantization (INT8/FP8/4-bit), batching & continuous batching
- vLLM, TensorRT-LLM, SGLang, llama.cpp & inference optimization
- KV caching, speculative decoding, prefix caching & token throughput
- Distributed training (DDP, FSDP, DeepSpeed, ZeRO)
- Model serving (Triton, vLLM, KServe, Ray Serve, SGLang)
- Kubernetes, Docker & GPU orchestration
- NCCL, InfiniBand & high-speed networking
- Multi-GPU & multi-node inference
- LoRA, QLoRA, PEFT & fine-tuning pipelines
- Vector databases, embeddings & RAG pipelines
- Prompt caching, semantic caching & cost optimization
- Observability (OpenTelemetry, Prometheus, Grafana, Langfuse, Phoenix)
- LLM evaluation, benchmarking & A/B testing
- Model routing & fallback strategies
- MCP, AI agents & workflow orchestration
- Data pipelines, Kafka & streaming inference
- Security, guardrails & prompt injection mitigation
- CI/CD for ML (MLOps), MLflow & model registries
- Linux, networking & storage fundamentals
- PyTorch internals, CUDA profiling & kernel optimization

[Roan @RohOnChain]

Met a guy making $1.4 million a year as a prompt engineer.

I asked him how he learned prompting so well.

He sent me a video that was never supposed to get out. Andrew Ng's 2 hour prompting course.

You wont find anything better about prompting than this video.

I watched it last night.

Halfway through, I realized I have been using Claude completely wrong for years.

Bookmark and watch this today.

[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]

[Harkirat Singh @kirat_tw]

Gold