JioStar’s chief architect on AI discovery at 500M-user scale
JioStar’s Vijay Seshadri explains AI discovery at 500M-user scale. Read his lessons on evals, guardrails, and taking AI into production.
#Deployment
#Deployment
0 todayNov 12
Nov 11
Designing for Agents — Zeno Rocha, Resend
Where does answer engine optimization (AEO) end and agent experience begin? Inside Resend, from the docs that help Claude Code pick an email API to the tools it uses to send.
Nov 10
The backlash about data centers might really just be about electricity
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. As voters went to the polls across the US last week, I was in Ashburn, Virginia, part of what’s known as Data Center Alley—the world’s largest data center hub.
Nov 3
Building Legal Agents When You Can't See the Data - Jacob Lauritzen, CTO of Legora
Legora can’t read a single trace of what its agent does for a lawyer. The data belongs to the law firm’s clients. How do you build and test an agent when you can’t see its work?
Building AI for Lawyers - Jacob Lauritzen, CTO of Legora
Evals without traces, million-cell tabular reviews, and cracks in the billable hour as a business model
Nov 2
Stop counting tokens, start counting what ships
When a developer gives an AI agent a coding task, the agent does more than answer once. It plans, generates code, runs tests, and repeats the process to correct errors. A single task can use 10 to 50 times more tokens than a basic chat prompt.
Oct 31
Sam Altman says it’s better for RAM to be in a data center than on your desk
Asked about supply chain shortages, like the ongoing RAM shortage, OpenAI CEO Sam Altman said in an interview that it's "terrible" to have RAM in personal PCs instead of data centers.
Oct 28
Pairing industrial AI autonomy and human control
The latest iteration of a legacy Founded at the Massachusetts Institute of Technology in 1899, MIT Technology Review is a world-renowned, independent media company whose insight, analysis, reviews, interviews and live events explain the newest technologies and their commercial, social and political impact.
How Anthropic ships the #1 LLM in the World safely: Separating the Brain from the Hands
Katelyn Lesse and Angela Jiang join Latent Space to explain the engineering behind the Claude Platform: launching Opus 5.5, separating the model’s “brain” from the agent’s “hands,” scaling APIs, and building reliable managed agents.
How Anthropic ships Claude with Claude: Separating the Brain from the Hands
Katelyn Lesse and Angela Jiang on Opus 5.5, the Claude Platform, and the engineering behind AI agents.
Oct 27
Industrial Intelligence: The next growth opportunity for manufacturing
Every manufacturer has its own industrial intelligence. Some is documented, and some resides in the knowledge of experienced workers. Consider an experienced operator preparing to restart a line after a changeover.
Supabase’s Plan for a Billion AI-Built Apps
AI agents are creating more software—and that changes what a backend needs to do.
Oct 22
You are the Team — Anekha Sokhal, Moshi
How Moshi turns market data into actionable insights, and why AI tools have to fit the trader’s workflow.
Making the case for context-aware AI
Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one.
Oct 21
AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess
Paying for outcomes, building with agents, and the last mile of enterprise AI adoption
The Programmable Data Center — Evan Conrad, SF Compute
From GPU trading to physical settlement, independent markets, and programmable compute
Oct 20
The new economics of enterprise AI
Imagine your company just discovered electricity. At first only a few machines run on it, and nobody worries about efficiency — consumption is low and the value is obvious. But once electricity becomes infrastructure, every process depends on it, and the question changes from “can we use it?” to “how efficiently are we using it?”
Oct 15
Trust in Depth: Giving agents more autonomy without losing control
Enterprise leaders want AI agents to take on more work but don't fully trust them to act correctly every time. Trust in Depth is AWS's layered approach-scope, know, bound, observe, and prove-that lets agents act on their own when their actions can be verified, reserving human judgment for the decisions that need it.
Oct 14
Dyna-2: General Purpose Physical Agents with Hour-Long Autonomy — Jason Ma, Dyna Robotics
Our first robotics pod with Dyna’s Chief Scientist on their big bet on World Action Models & the Next Frontier of Physical AI
Oct 11
Cheaper AI tokens are driving more demand, and that's Jensen Huang's best-case scenario
Data from a16z shows a Jevons paradox in the AI market: token prices keep falling, but H100 GPU rental prices hold steady or climb. Cheaper AI drives demand faster than costs drop. If that demand flattens, the chain from chip makers to cloud providers takes a hit.
Oct 4
We're going to need default hard budget caps on pretty much everything
Here's a product feature which the world is going to need a whole lot more of over the coming months and years: default hard budget caps . I'm talking about the feature of pay-by-usage services and APIs that lets you say "after $X/month, cut this thing off and return errors". These need to be hard limits.
Oct 3
Redefining enterprise intelligence with autonomous AI
Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year.
Sep 29
Making AI an asset, not an expense
When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes.
Sep 25
When chat is the wrong UI
What is a developer to do when they need something more tangible than a chat box? Enter canvases.
Sep 22
Jev: System One models for Prod, not God — with Diogo Almeida, CEO, TypeSafe AI
The definitive Jev podcast with its lead creator.
Sep 21
AI Security Is an Engineering Problem — How to Solve It at Every Layer of the Agent Stack
AI security is an engineering problem. That means defined security requirements, enforceable controls, named owners and evidence that protections work. As AI becomes more capable, the industry must accelerate security engineering, broaden access to defensive tools and share what works faster.
Jul 30
GPU Management: Why Idle GPUs Are the New Grounded Aircraft
Hugging Face's blog argues AI's next real constraint is GPU utilisation, rather than intelligence. GPUs are billed by calendar hours but produce only in compute hours, mirroring the cost structure of grounded aircraft.
Jul 7
Intelligence is Free, Now What? Data Systems for, of, and by Agents
... government of the people, by the people, for the people ... — Abraham Lincoln, Gettysburg Address (1863) The cost of AI is dropping rapidly. GPT-4-class capabilities cost roughly $30 per million tokens in early 2023; today the same runs under $1 , and some providers are pushing costs below $0.10 .
May 8
Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling
Overview of adaptive parallel reasoning. What if a reasoning model could decide for itself when to decompose and parallelize independent subtasks, how many concurrent threads to spawn, and how to coordinate them based on the problem at hand? We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel Reasoning.