Amrut Rajkarne

🇮🇳🥇🥇🥇🥇🥇 ALL FIVE Indian students win GOLD at IPhO 2026 — India ranks World #1 (joint #1) Having trained at IMOTC myself, I know exactly how brutal that grind is. Massive respect to Kanishk, Riddhesh, Rishit, Shresth, Svarit and @HBCSE_TIFR for building this pipeline year after year.

Imagine a fable 5 quality model that’s 3-4x less expensive in less than 6 months. And an Opus 4.8 grade model that can run on a local device in less than 12 months. Greater than 50% chance that these events will happen. Worth keeping in mind when you make predictions about the future.

1/ muse spark 1.1 is an industry-competitive agentic and coding model. across many agentic evals it rivals gpt-5.5 and opus-4.8. available now through the new meta model api and in meta ai. 🧵

@battarchicken @aoagents @NexusVP @suraj_markup Great having you demo Agent Orchestrator!

couple of days back had the opportunity to present @aoagents at the @NexusVP office in Koramangala along with @suraj_markup demoed AO from starting the app to spawning multiple agents which created a feature, built the frontend UI for it, pushed the code, created a PR, reviewed the PR and pushed the review on GitHub, all of this while I just monitored them from the Kanban Board. got a lot of great and mindful feedback from the diverse set of awesome people at the event. Thanks to all of them :) one of the most inclusive events i have been to surely, and thanks a lot @FrontierAmrut for providing this stage at AI Tuesdays at Nexus :)

We raised $136M to kill Slack. Introducing PromptQL: The first AI version of Slack. Here’s how it works:

The next frontier of AI isn’t bigger models. It’s provable intelligence. @PramaanaLabs is building that frontier. @ranjan_vittal covers this aptly in his recent interview with @CNBCTV18Live #ProvableAI #FrontierLab #AI #HallucinationFree

Math proofs are cool, but the real revolution? Verifying real-world claims in accounting, tax law, compliance, medicine & more; reliably, efficiently, and at scale. Proof trees look different across domains. One-size-fits-all won't cut it. AI prover architectures must be purpose-built to navigate the specific reasoning trees, resource constraints, and rule stability of the environment they are verifying. Read why specialized provers are the future → pramaanalabs.ai/blog/the-age-o… Authored by: @ArnavAMehta Watch this space for more to come from our Technical blog-post series.

🔗 luma.com/v7rljhjn

The most valuable AI workflows don’t show up on benchmarks. They live in terminals, browser tabs, and internal automations. We’re bringing those builders together for an AI Builders Show & Tell. No panels. No pitches. Just live demos. 📅 July 7, 6:30 PM (link below) @NexusVP

We've kept hearing how GLM-5.2 beats Opus 4.8, and are skeptical of benchmarks - so we tested them on a real bug from the Cline repo. While both models fixed the issue, GLM was the winner in terms of cost and code quality: - GLM used twice as many tokens (GLM 1.1m vs Opus 660K) but cost half as much (GLM $0.41 vs Opus $0.81) - Opus finished quicker - 1.6 min and 12 tool calls vs GLM 4.7 min and 28 tool calls - GLM cleaned up dead code and verified the build compiled before completing. Opus didn't - it left type errors that passed tests but broke the production build. Both runs used the same Cline harness prompting and tools, so it seems GLM is RL trained to spend more tokens verifying its work before completing. Impressive work by the @Zai_org team!

There’s a big misconception about how GLM 5.2 was trained. Yes, they distilled Claude and GPT 5.5 — but distillation is not how they matched Opus quality. Distillation only fixed the cold start problem in RL. RLing an agentic coding model isn’t rocket science. In simplified terms: 1. RL needs trajectories — rollouts where the model actually completed a task in some env 2. No successful trajectory on a task = zero gradient = you can’t RL it. This is the cold start problem 3. Distillation solves it. You seed your model with knowledge from a smarter one (Claude, GPT) on tasks it can’t do yet 4. Now it produces positive trajectories on those tasks 5. RL on those trajectories and hill climb agentic coding 6. At that point you no longer need to distill and can solely hill climb RL to better models This is an interesting curve. I’d argue it’s harder to get to Opus 4.8 from scratch than to go from Opus 4.8 → Fable/Mythos tier. GLM 5.2 is already producing positive trajectories, so they have plenty to RL on — they’ll keep climbing to Mythos quality without distilling any further. They no longer need American models.

Thank you very much for the shoutout @svembu! This means a lot and we have been going deep and building domain-specific programming languages to capture strict nuances in areas like law and tax. Exciting times ahead! PS: I was sitting right next to @sanjaygsub during your inspiring talk at IITM :)

Another new idea to push the state of AI architectures forward. Sakana released a model that effectively uses a mixture of models to get work done. You get a single API but then the work gets farmed out the model that best performs the task. “Fugu manages model selection, delegation, verification, and synthesis automatically. It solves tasks directly when that is enough, or coordinates a team of expert models when a problem calls for more. The complexity of a multi-agent system never reaches your code.” This is generally how applied AI products are building their agent harnesses at this point, but the idea of making this an LLM that any developer can interact with is also a great idea. As we get more innovation with both frontier closed and OSS models, there’s going to be a ton of value produced for the layer that can route the best.

Excited to be day 0 backers of @PramaanaLabs. Onwards and Upwards @ranjan_vittal 🚀🚀 @NexusVP @b_jishnu

@krishnan_rag Great thoughts and great articulation of the problem in AI and how we are building provable AI systems @PramaanaLabs. Such incredibly hard problems can be solved only by deep olympiad folks 😃

Billionaire Michael Milken joked “if a US company replaces the US-born CEO with a CEO born in India, I buy the stock” But he reveals he hasn’t backtested the idea. So we did. In the last 15yrs, that would’ve 50x’d your money: 7.5x more $$ and >2x IRR vs S&P500: 30% vs 14%!

@sarwal_varuni and @rosemary0680 are building an incredible healthcare AI platform at @Trifetchai to take away the administrative burden from clinics. We are very excited to have led their pre-seed raise and thrilled to be partners in their journey

Introducing TriFetch: your clinic's first AI employee. Only do the work you want to do; give AI everything else. We will change the US healthcare system, forever. @Trifetchai @rosemary0680

AI can be made deterministic. @ranjan_vittal and the team at pramaanalabs.ai are pushing the frontier bringing determinism to AI by using Lean4 based formal verification to tackle the complexity of US tax codes.