I said on the day the RBI letter landed that the real change from a Tata Sons listing would be scrutiny of how TCS profits fund Air India losses. Business Standard has now gone through the FY26 annual report and put numbers on that sentence.
I think this is the most important table of the whole story.
NVIDIA just released the NVFP4 quantized Qwen3.8-Flash-Next on Hugging Face
125B MoE with hybrid attention, now 63% smaller with minimal accuracy loss.
If NVIDIA decides that these models available on Hugging Face are optimized first for its hardware, other future AI hardware companies are going to have problems even capturing even a minuscule market share.
Nvidia has reportedly agreed to buy Hugging Face, the popular open-source AI hub, for $12.9 billion in a move that would let Nvidia both protect its chip empire and jump back into the cloud business.
https://t.co/v9gaZr4Kdd
Anthropic CEO Dario Amodei has expressed concern about new talent joining the firm for financial compensation rather than alignment with the company's core safety and research mission, per Axios
ORBIT ACHIEVED. 🚀
Vikram-1 Test Flight-1 has reached orbit. India's first privately developed orbital rocket has completed its final burn and injected its payloads into a ~450 km orbit, making India the third country in the world with private orbital launch capability.
History is made. 🇮🇳
#Vikram1 #JourneyToOrbit #SkyrootAerospace
Introducing Kimi K3: Open Frontier Intelligence
🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows
Kimi K3 is now live on on https://t.co/zrk6zZxZUo, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.
🔗 API: https://t.co/XCrgjXAqMw
🔗 Tech blog: https://t.co/YTfiMSNM1f
We’ve designed and built our first AI chip: Jalapeño.
Designed from the ground up by OpenAI and brought to production with @Broadcom, Jalapeño is purpose-built for the LLM workloads powering ChatGPT, Codex, the API, and future agentic products.
Chips are foundational to the AI economy. Building our own expands our full-stack platform from products to models to infrastructure, and will help us scale intelligence, serve more people, and expand access to AI.
ResNet and YOLO received to the Longuet-Higgins Test of Time award. Congrats!
Three thoughts:
- very “difficult” job for the committee this year
- people are still using both quite a bit
- time for an additional generation to feel old — I already got used to that 😅 #cvpr2026
If we’re really that close to AI replacing huge parts of the workforce, I’d expect these systems to already handle complex real-world engineering problems reliably. Dario should take on some serious challenges and demonstrate the results.
Anthropic’s CEO has every incentive to push the idea that AI will replace every second job.
That story makes his company look bigger, more important and more profitable to investors who are pouring billions into this space and expecting huge returns
I’d be careful with claims like that, especially when they come from people selling systems trained on flawed human knowledge ..systems that are sometimes dead certain while being dead wrong
Using AI to solve the Traveling Salesman Problem at warehouse scale. 📦
AlphaEvolve helped FM Logistic improve its routing algorithm by 10.4%, resulting in a reduction of total warehouse travel by over 15,000 km per year. 🚚
A great example of how @GoogleDeepMind and @googlecloud are using AlphaEvolve to help companies become more efficient.
Read more at: https://t.co/19KvSOoTpk
During the initial days of computing, an inverted pyramid was likely true. IBM, Intel, and Cisco were richer. Thanks to pioneers who democratized hardware and software, hardware became cheaper and resulted in the pyramid that we see now. Pyramid for AI can invert too.
It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.
Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes.
As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now.
It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.
New work: Do transformers actually do Bayesian inference?
We built “Bayesian wind tunnels” where the true posterior is known exactly.
Result: transformers track Bayes with 10⁻³-bit precision. And we now know why.
I: https://t.co/e5O5O3QN5U
II: https://t.co/11KMePLUaT 🧵
Bengaluru’s Second Airport: On Track for 2033
We are firmly committed to building Bengaluru’s 2nd international airport. The project is on track. AAI has inspected 3 shortlisted sites (Kanakapura Rd & Nelamangala). Based on AAI's feedback, a feasibility report will be prepared.
With the Centre’s approval expected soon, we aim to shape the project by 2030 and target operations by 2033. Tamil Nadu’s Hosur plans will not affect Karnataka’s vision. Our focus is clear: ensuring Bengaluru’s long-term passenger and cargo growth.
#secondairportforbengaluru
I met a founder today who said he writes 10,000 lines of code a day now thanks to AI. This is probably the limit case. He's a hotshot programmer, he knows AI tools very well, and he's talking about a 12 hour day. But he's not naive. This is not 10,000 lines of bug-filled crap.
🤖 What if a humanoid robot could make a hamburger from raw ingredients—all the way to your plate?
🔥 Excited to announce ViTacFormer: our new pipeline for next-level dexterous manipulation with active vision + high-resolution touch.
🎯 For the first time ever, we demonstrate ~2.5 minutes of continuous, autonomous control—combining active vision, high-res touch, and high-DoF robot hands SharpaWave — to complete complex, real-world tasks.
Code is fully released; check out our:
Homepage: https://t.co/I8kVJO3XPw
Paper link: https://t.co/BFqpOwXYbT
Github: https://t.co/345eTpTx0y
New model drop - Sarvam-Translate is here. Can translate between 22 Indian languages & English. Significantly better than much larger models. Improves on nuance, long-form, structured text. Available as super-fast APIs. Try it here: https://t.co/9n6rm2sb4c
Leaving current debate and advantages aside, some people really struggle with languages. In these cases, a single language is sufficient for them to be better off instead of many. It's like, if you are weak at maths, I would make you go through more such subjects for gdp growth.