Angel Investor | Global R&D Leader - Cloud | Data | AI/ML | RF |Semicon | EDA | S/w Architecture. Itinerant. Distance Runner.
Bangalore
Joined October 2007
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Created a list of around 100 books that had major impact on me over the last two decades. These are also the books that I've re-read several times. buff.ly/2xaaDLd
RamPrasad "RamP!" Moudgalya retweeted
IMO the only tests you should have in the age of coding agents are, in order of priority:
1. Full E2E tests. Nothing mocked out at all. Can even be something that runs in prod on test accounts via Playwright or equivalent.
2. Integration tests. Agents make more mistakes as things bleed between data/API boundaries where schemas can drift.
3. Golden tests. This helps ground the code with real examples of data, and can be used as regressions against edge cases.
Unit tests are bloat 99% of the time if you’re using a frontier model since they’re smart enough now to get the implementation (and subsequent iterations) right on the first shot, so they only add bloat at this point.
RamPrasad "RamP!" Moudgalya retweeted
Today we’re announcing Personal Agent Protocol — an open standard @Meta and @SierraPlatform are developing along with industry partners at @Genesys, @instinct, @RocketOTD, @Shopify, @stripe, and @Walmart. It will help define how personal agents interact with businesses and is open for anyone to implement. You can read more here - and if anyone is interested in joining let me know! sierra.ai/blog/introducing-p…
RamPrasad "RamP!" Moudgalya retweeted
Today we're releasing our AI SDLC transformation playbook, where we share best practices to deploy AI at enterprise scale
Engineering leaders frequently ask how Atlassian thinks about our AI-native SDLC. This playbook is our answer, informed by our own transformation and learnings from top engineering organizations we work with
Come find us at the AI SDLC booth at Team '26 EU this week for a physical copy (and merch) atlassian.com/blog/ai-at-wor…
RamPrasad "RamP!" Moudgalya retweeted
Ben Horowitz on what he expects in AI over the next 8 to 12 months.
He thinks the infrastructure for AI is wrong, and it reminds him of the early days of cloud computing. Back then, everyone thought they could put their existing software on the other side of the wire. It didn't work, and everything got rebuilt: storage, networking, operating systems, virtualization, containers.
This is why he expects the same with AI:
1. The workload. A database query is predictable. An AI query could run for a week, and the systems to manage those requests don't exist yet.
2. The chips. Nvidia's GPUs were built for video games. Ben calls them power-hungry and low-yield for what they do today.
3. The models. Training them means sucking down all the data on the internet, and he doubts that is where we end up.
His conclusion: from the model on down, everything is going to change, and there will be a lot of opportunity in that.
Full breakdown here: theaiopportunities.com/p/ben…
"Most people aren't looking to save time, they're looking for ways to spend their time."
9 of 15 consumer internet categories have zero AI products in the Top 100. These built some of the biggest companies of the last two eras:
- Streaming
- Social
- Dating
- Gaming
- Travel
- Retail
- Finance
- Real estate
- Jobs
More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-consumer…
The seventh edition of our Top 100 Consumer AI Apps is here.
New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings.
Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else.
In today's edition:
- ChatGPT still holds the throne, now with 1B+ monthly actives on mobile
- Claude has climbed to #3 on web with nearly 1B monthly visits
- The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video (Higgsfield, Kling), agents (Manus), and even hardware (Plaud)
Full breakdown from @omooretweets: a16z.news/p/top-100-consumer…
RamPrasad "RamP!" Moudgalya retweeted
BREAKING: Ben Horowitz just mapped the AI market.
The a16z co-founder has lived through every platform shift since the PC. He says this one is the biggest: AI is a new computer, reinvented in two years.
His map starts with ElevenLabs. It makes AI voices, a space where OpenAI and every big lab were a direct threat. Last week it hit a $22B valuation.
Ben explains how, and uses it to show founders where the market is going.
The users and the revenue arrived together with the money, which is why he doesn't see 1999. For the first time, money can buy a lead: xAI got close to OpenAI in about two years. The moats that hold are the ones money can't buy fast. Customers can now rebuild a lot of their own software. And the infrastructure under AI will be rebuilt from the chips up, the way cloud was.
Five shifts, one map of where to build next.
theaiopportunities.com/p/ben…
RamPrasad "RamP!" Moudgalya retweeted
Handbook on Understanding Harness Engineering for AI Agents [48-page PDF]: drive.google.com/file/d/1PT-…
RamPrasad "RamP!" Moudgalya retweeted
Last week I shared the list of Bangalore unicorns.
Today, I am sharing a few global ones hiring very actively in Bangalore 🇮🇳
There are many but here are the top 10 of the lot, hiring aggressively:
- @databricks (79 open roles)
- @stripe (42 open roles)
- @AWNetworks (34 open roles)
- @Celonis (32 open roles)
- @FNZ_Group (27 open roles)
- @saviynt (24 open roles)
- @glean (24 open roles)
- @OneTrust (21 open roles)
- @SkildAI (12 open roles)
- @kong (9 open roles)
300+ open roles in Bangalore across these 10 alone. And a lot more in the other 50+ unicorns and hundreds of startups around them.
If you're actively looking out or planning to move to Bangalore soon, join thousands of job-seekers who've used the platform to find their next gig at NextDoor.Company
💬 Which global unicorn in Bangalore did I miss?
🔗 Tag your company if hiring actively in the comments 👇
RamPrasad "RamP!" Moudgalya retweeted
Anthropic engineer:
"90% of our engineers were already running self-improving loops
Now everyone is moving toward agentic graphs"
"Prompting is basically finished"
In just 10 minutes, she builds her complete Claude Code setup live from an empty terminal
Agents → Loops → Graphs → Self-Improving Systems
Prompting was the old workflow
Graph engineering is the next one
This 10-minute breakdown is worth more than most $1,000 agent engineering courses
Watch it today
Then save the full guide below before everyone catches on ↓
RamPrasad "RamP!" Moudgalya retweeted
Andrej Karpathy revealed what survives after prompting
"Prompting is fading away
Delete everything else and keep the graph"
In a 1-hour lecture, he explains how to build graphs and why they are the layer everything ends up becoming
LLMs → Prompts → Agents → Graphs
Most people are still trying to perfect prompts
The graph is the part that survives
Everything before it is just a step in the process
This free lecture is worth more than most $500 graph engineering courses
RamPrasad "RamP!" Moudgalya retweeted
We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:
Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:
Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:
Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:
Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!
In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.
OpenAI announces dots.
I have been testing it for the last few days and it’s something special.
Proactive capabilities are really great.
I used it to organize my entire DevDay from travels to session/meetings setup.
It has its own computer.
Has come great note taking tools for organising work and it’s only going to get better for collaboration.
A lot of work ahead but it’s already fun and useful.
How to self-host your dev environment and run multiple coding agents on your project.
This is open-source and free to use.
You can run it on your computer or a server in the basement.
• Multiple agents
• Configurable Templates + Workspaces
• Open and free
RamPrasad "RamP!" Moudgalya retweeted
We instituted an official AI Writing Policy at @Clay.
Massive thanks to @sophiebits on our engineering team who wrote this.
Originally this was just for eng, but other teams found it so helpful we expanded it company-wide.
Here are the four guiding principles:
1. You must stand behind every idea and sentence
It is your responsibility to make sure that the entire document is representative of your own thoughts before you share it.
2. Writing is thinking
Spending time on the writing process teaches you more about your topic. If you circumvent this process, you will walk away with a poorer understanding of the subject matter.
3. More time should be spent writing a document than consuming it
If you generate a document from a short prompt then ask your readers to go through the longer output, you are disrespecting their time. They can talk to ChatGPT themselves if they want to.
4. Longer is not better
AI makes it easy to generate long docs, and it loves padding them with sentences that say nothing. If you're producing docs from a short prompt, consider just sharing the prompt.
You can read (and borrow) the full policy below. Hopefully we all communicate a bit more clearly now :)
RamPrasad "RamP!" Moudgalya retweeted
Today we’re introducing Base Code.
It’s an early preview of how we think software will be built in the future.
It’s a product that encapsulates everything we’ve learnt from:
- How our users are building software.
- How we’re building internally, scaling to hundreds of millions of dollars in revenue while keeping our engineering team very small and focused.
Here are some of the principles behind it, which align with how we at @Base44 think about the future of software engineering:
Cloud:
- Software will move past local desktop environments (where current tools are widely used) and move to the cloud.
- Moving everything to the cloud is not easy. Setting up dev environments with databases, infra components, services, mock data, etc. is easier said than done.
- Base Code first scans your code repo and sets up everything - every infra component (databases, Redis, etc.) - in the cloud. It runs nonstop until your preview environment is ready.
- From an enterprise standpoint, it’s also the logical thing to do: a centralized, governed dev environment instead of handing out keys and secrets to all team members.
- Once Base Code does that, EVERY team member can work in this environment. No more syncing local environments.
Internally, this (cloud) is one of the main things that enabled us to move so fast.
Collaboration:
- Once everything is in the cloud, everybody can write software from anywhere (any browser, your phone, WhatsApp / iMessage coming soon).
- You can easily see what everyone is working on, where they’re at, and how they’re prompting - and jump to their environment in one click.
- We’ve built many great collaboration features from the ground up.
Loops, automations, software factories:
- A full, working cloud environment allows for many advanced capabilities we will unveil soon-think agents running in the browser and testing on every device and in any browser.
- It also allows the use of automations to build software factories-e.g., agents reading support tickets, identifying bugs to fix or features to develop, implementing the changes, verifying them in the cloud, and potentially pushing them.
And lastly, there’s a real advantage in being model agnostic.
--------------
As always, we’re releasing it very early. It’s far from perfect-but we’re looking for early feedback so we can build it together with our great community and make this vision a reality.
*We’re giving it away for free for 30 days*.
Give Base Code a try. Connect any GitHub repo, get your live preview, and share it with your team.
I’d love to hear what works and what doesn’t:
app.base44.com/base-code
🤖 Made with AI
If you are in serious Agentic AI development, it is worth watching this one hour keynote by David Heinemeier Hansson (DHH), on shifting paradigms in coding, the role of intelligent agents in the future of programming, during his keynote address during Rails World 2026
youtube.com/watch?v=vDjW_dRy…
RamPrasad "RamP!" Moudgalya retweeted
Yann LeCun, Executive Chairman of AMI Labs, explains why LLMs are mostly retrieving human knowledge rather than thinking for themselves:
LeCun starts with why so many people misread what these systems are doing:
"I think there's a lot of confusion, really, because we tend to anthropomorphize systems that can reproduce certain human functions."
When an AI writes fluent answers, we assume there's a mind behind them. LeCun's view is that most of what we're seeing is something more familiar:
"LLMs, to some extent, except for a few domains, are mostly information retrieval systems. They can compress a lot of factual knowledge that has been previously produced by humans and can give easy access to it."
The key words are "previously produced by humans."
The knowledge in an LLM came from people. What the system adds is compression and easy access.
That puts LLMs in a long historical line:
"In a way, it's kind of a natural evolution of the printing press, the libraries, the Internet, and search engines. Right. It's just a more efficient way to access information."
@ylecun is clear about their value:
"LLMs are incredibly useful, there's no question about that. And they do amplify human intelligence, like computer technology going back to the 1940s."
He also allows that in a few areas, such as generating code and some types of mathematics, the capabilities seem to go beyond retrieval. But he notes what those areas share:
"It's still, to a large extent, domains where reasoning has to do with manipulating symbols."
That's where the retrieval framing shows its limits. If these systems were truly thinking for themselves, you'd expect that ability to carry over into the physical world. It hasn't:
"The problem is that why do we have systems that can pass the bar exam and win mathematics Olympiads, but we don't have domestic robots, we don't even have self driving cars."
Then comes his sharpest comparison:
"And we certainly do not have self driving cars that can teach themselves to drive in 20 hours of practice like any 17 year old. So we're missing something big still."
RamPrasad "RamP!" Moudgalya retweeted
Jensen Huang: don’t mistake engineering vocabulary for evidence of a machine mind. AI is still software, not a human mind inside a machine.
"We can't make jokes about all this stuff, we're scaring the American public."
Words like “spawn,” “parent,” “child,” and “kill” have existed in computing for decades; Giving those same mechanisms human characteristics today because of AI is unnecessary and misleading.
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From "The Ezra Klein Show + New York Times Opinion + New York Times Podcasts" YouTube channel, (full video link in comment)
Exciting work from NVIDIA.
(bookmark it)
Interesting to see this approach to turn public Agent Skills into RL environments.
Lots of excitement around RL environments so this is a great read.
Skill2Env compiles each Skill into executable terminal tasks.
A Codex planner reads the SKILL.md bundle, researches related public assets and splits the Skill into workflows.
A Codex creator then builds each task with programmatic tests and a behavioral rubric taken from the Skill's own quality criteria.
From about 3.4k crawled Skills, the pipeline produced 7,971 tasks across 13 domains, with software engineering under a quarter of the corpus. Generating them with GPT-5.6 Sol cost over $90k in API usage.
After 300 steps of outcome-only RL, Qwen3.8-27B improved from 49.4% to 54.1% on Terminal-Bench 2.1 and from 33.4% to 37.7% pass@1 on S2EBench, their hand-verified held-out benchmark.
Adding the rubric to the reward gave smaller benchmark gains, 50.1% on Terminal-Bench 2.1. Given the source SKILL.md, a judge preferred the rubric-trained model's trajectories over the base model's on 73.0% of tasks, against 54.5% for the outcome-only model.
Paper: github.com/NVlabs/Skill2Env/…
Chat with Paper: academy.dair.ai/papers/reinf…
