aacash.eth - Aakash Kumar

Guess who is number 2 on the @ArtificialAnlys Speech to Text benchmarks for speed. And the one that is number 1 is way worse in quality. The Smallest AI team churned out one of the fastest, most efficient speech-to-text models ever created.

To all @ycombinator S26 applicants ! May the fourth be with you! S25 we @DeVC_Global partnered with 6 cos, looking forward to S26 keenly. Those looking to apply to future cohorts, reach out to @Rahul_J_Mathur from the team and get plugged in with our growing fam of YC founders for application guidance.

"Useful primitives are discovered, added to the harness, and then used when training the next generation of models. As this cycle repeats, models become more capable within the harness they were trained in." Model - harness fit isn’t a light abstraction, it is the reality of how the new stack will form.

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today. The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do. First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents. Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do. Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes. Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design. All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it. This is a huge opportunity right now whether you’re doing this internally or as an external business provider.

Datadog = Customer is 🐶 Always has been, will be. Amazing to see even the abundance mindset world which operates from ‘no fucks given’ attitude towards efficiency is now switching out. Real Engg teams in hungry India never fell for the trap.

Agents in production will strath churn the org from many tools/platforms. In an ideal world with strong harnesses deployed at cos, very few from the old stack survive. This is the way. Either you are delivering HaaS: (Haness as a Service) for your customer OR you are mission critical infra for the HaaS. Rest is mostly an illusion of PMF.

Another team shipping at warpspee! And focusing on a substrate that I cortical to a harness. LFG 🚀

Congrats @thesuyashsingh and team @GalaxEye Cheers to many more in the future!

@siddontang This is a very apt framing. An agent plays the role of both data engineer, analyst, data scientist, business user : All in one. Also, that’s why databricks’ acquisition of neon has potential to go down in history as one of the best acquisitions ever.

@anshublog 💯

“Agents will generate workflows dynamically. Applications will get thinner. And the systems that manage memory, state, coordination, and history will become more important than ever. Which is why I think databases are moving back to the center of software architecture. Not as storage. As runtime.”

@paraga 🤚

museum.parallel.ai/items/twitter-… ✋ if you remember

Introducing Public Agents It’s the easiest way to share an agent you’ve built, or install one someone else made Teams are already using agents to: - audit and secure apps - run interviews/screen candidates - run operations for businesses - and 100+ examples Try it now for $0

For 50 years, software engineering ran on code rationing. Writing code was expensive, so we rationed it carefully through roadmaps, RFCs, prioritization meetings, and scope reviews. This created a role: the No Engineer. No, that won't scale. No, we don't have bandwidth. No, that's out of scope. No, we need a design doc first. The No Engineer was valuable for 50 years. Every "no" saved real money. Their judgment was the rationing system. LLMs will be the end of code rationing. Code is cheap now. And while the No Engineer is explaining why something can't be done, the Yes Engineer has already shipped three versions of it. If you're a Yes Engineer, the next decade is yours.

@miten If only LLMs had animal responses too. Need and alternate of ‘punish hard and publicly’ for agents too.

federating is an art form. set principles, enable creativity. allow deviations. punish rarely, but punish hard and publicly. rinse, repeat, scale!

💯 a great product for agents! And they are just getting started. Biggest miss as a VC for me, for reasons that hurt even more. Kudos to the team! @adisingh

Fab! Though Websockets over gRPC can’t be so obvious a choice (?) I’d expect longer run gRPC gets picked for typed streaming and better ‘runtime’ efficiency. Maybe even both fused elegantly together is where this heads: Websockets at edge and really rpc internally.

Starting today, agents can now be Cloudflare customers. They can create a Cloudflare account, start a paid subscription, register a domain, and get back an API token to deploy code right away. cfl.re/4sY0Uxn

Fml 🤦🏽‍♂️ I don’t want to go back down this rabbit hole after 15 years! But also 🤩

We just removed the waitlist on projects.dev! Also 14 new providers (now 32 total). You can instantly provision all of them from the CLI.

Smallest AI is now natively supported in @pipecat_ai Lightning TTS + Pulse STT can now plug directly into your Pipecat voice agent pipeline. Docs below ⬇️