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AI for Developers
Tools, techniques and ideas for building software with AI.
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I Slopped Up the Internet: Here's How I Did It
The user built an AI-powered SEO and AEO automation system called Content Empire, which generates and deploys content at industrial speed, producing a portfolio of 59 domains and 6,306 published articles. The system aims to outrun Google's whack-a-mole system and produce lasting results in the AEO game.Read All
We are going to cfgmgmtcamp.org
Conference plans, agent harnesses, AI-assisted Terraform reviews, and the engineering practices that make delivery faster.
UNBOUND 2026 Recap: HubSpot’s Next Era of AI-Powered GTM
Explore HubSpot’s biggest UNBOUND 2026 product updates and what they mean for AI-powered GTM, pipeline creation, revenue growth, and customer context.
Si Sheppard – How did a few hundred Spanish soldiers topple two empires?
Fall of the Incas & Aztecs.
The Caveman Prompting Challenge
Caveman prompting has one rule: why use many words when few do the trick? It saves tokens on the way in and on the way out. Push it too far, though, and the output falls apart. So how far is too far? Nobody has benchmarked it yet, and that question opens our conversation with James Barney, Head of Forward Labs at MetLife.James spends his days connecting new AI capabilities to old business problems across dozens of regulatory regimes, and he still finds time to push code.
Maximor Rebrands as Hyphenate After 86x Revenue Growth Across Five Finance Functions
Maximor has rebranded as Hyphenate after revenue grew 86x in twelve months, following a $9 million seed round led by Foundation Capital. The platform now covers five areas of finance: order-to-cash, treasury, GL accounting and close, procure-to-pay and reporting and insights. The average customer runs six modules and 40% expand within their first year. Autonomous finance means software that owns a workflow through to an outcome and brings in a person only when a call genuinely needs judgement.
SE 30: Todd Barr, CEO at Axonis | Live from Ai4 2026
Send us Fan Mail Todd Barr is the CEO of Axonis and has spent his career in high growth software infrastructure, living through the shift from Unix to Linux, the rise of DevOps, and the blockchain era before landing in AI.
An Animated Guide to ReAct Agents
Ask a plain language model a tricky question and it might confidently invent the answer. Give it tools and a loop, and it can actually work the answer out. In this animated explainer, we break down ReAct, the pattern behind many AI agents, and watch one solve a real question step by step.
Open models and the future of Physical AI with NVIDIA
As AI moves beyond the cloud and into robots, vehicles, and other physical systems, new approaches to models, simulation, and inference are emerging. Daniel and Chris are joined by Ming-Yu Liu, Vice President of Cosmos Lab at NVIDIA, who shares his perspective on open models, world models, and the future of physical AI. They explore why open models matter for innovation, how world models can help AI understand and simulate the physical world, and what it could take to build increasingly capable physical AI systems.
Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week
Our DevDay coverage - the first pod on the DevDay lineup - dives in with the leaders of OpenAI’s CUA team and API platform.
Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week
Three months ago Dwarkesh, who has been posting incredible blogs and episodes about RL, posted a framing question for his video essay on RLVR which upset a lot of Computer Use folks:
Amazon S3 Tables now support all Apache Iceberg V3 data types
Amazon S3 Tables now support all data types in the Apache Iceberg V3 spec, along with column default values, deletion vectors, and row lineage. Upgrade existing V2 tables or create new V3 tables with built-in compaction, maintenance, and engine compatibility.
Give Your AI Agent Fast Reflexes
Why Your AI Agent Needs Reflexes, Not Just a Brain
Amazon S3 Vectors now supports metadata pre-filtering for higher recall on filtered searches
Amazon S3 Vectors now supports metadata pre filtering, delivering up to 5x higher recall on filtered searches. Filters evaluate before similarity search so scoped queries return more relevant results. Ideal for RAG, agentic apps, and document search.
Gemini 4 Argon: our next era of frontier intelligence
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work, and cyber defense, rolling out soon.
Traditional UX: Is It Coming to an End?
Traditional UX as we know it may be largely extinct within the next two years, replaced by a more integrated discipline that combines user experience with technical skills. The future of UX will require practitioners to evolve and expand their definition of design, connecting user needs to technical possibilities, business outcomes, data, and implementation.Read All
Amazon Aurora PostgreSQL now supports direct querying of Apache Iceberg and Parquet data in your data lake
Amazon Aurora PostgreSQL now lets you directly query Apache Iceberg and Parquet data stored in your data lake alongside live operational data—no ETL pipelines required. Powered by DuckDB embedded within Aurora, this capability enables single queries that join transactional and historical data using familiar PostgreSQL syntax. It supports AWS Glue Data Catalog, S3, and S3 Tables, with optimizations like predicate pushdown and caching for efficient performance.
Celebrating Our Newest AWS Heroes – September 2026
Today, we’re excited to introduce the newest members of the AWS Heroes program. AWS Heroes are a vibrant, worldwide group of AWS experts who go above and beyond to share knowledge, mentor others, and build thriving communities. These individuals make a real difference in helping developers and organizations succeed with AWS. This month, we welcome
Distributed databases with Peter Mattis
Cockroach Labs co-founder Peter Mattis discusses building reliable distributed systems and how AI helps him write more code without sacrificing quality.
Does your work cost less on Opus 5.5 now that its tokens are 20% cheaper? My 3-prompt guide shows which jobs do before you move them
With Opus 5.5, I can give the model direction, make a correction, and keep developing the thing I wanted. That sounds ordinary until you’ve spent a session explaining the same requirement repeatedly and wondering why a system this capable is so difficult to work with.
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