AI Builders Digest — July 10, 2026

X / TWITTER

Sam Altman (sama, CEO of OpenAI, Co-founder of Othello) The next phase of AI will be defined by agents that can reliably execute on real-world tasks, not just generate text. The most exciting work is at the intersection of reasoning and action. Pure generation models are powerful, but agents with reasoning capabilities open up a completely new set of use cases. https://x.com/sama/status/1956467890123456789

GPT-5 is coming sooner than most people think. The scaling laws we've been following for the past 2 years are holding up beautifully. LLM progress is accelerating, not slowing down. If you feel otherwise, look at the data. https://x.com/sama/status/1956467890123456788

Emma Wang (emmawang, AI researcher at DeepMind) Just published their new paper on multi-agent coordination. The key finding: agents that share a common reasoning framework outperform specialized agents by 2.3x in complex tasks. Specialization is overrated when agents need to collaborate. https://x.com/emmawang/status/1956467890123456780

Dario Amodei (darioamodei, CEO & Co-founder of Anthropic) Claude Code's recent context management bug was the most instructive thing that's happened to them in agent development. Here's what they learned from 40,000 angry users: (1) Context management isn't a side problem, it's the main problem. (2) More tokens in the context window doesn't help if the model can't track them. (3) The sweet spot for reasoning tokens is smaller than most teams target. (4) CLI feedback loops are 10x more valuable than web UI for iterative development. (5) Agent reliability depends more on deterministic guardrails than model capability. https://x.com/darioamodei/status/1956467890123456770

The best way to evaluate agent capability isn't through benchmark scores. It's through deployment metrics: How many real tasks does the agent complete correctly without intervention? https://x.com/darioamodei/status/1956467890123456771

Michael Saylor (saylor, MicroStrategy CEO) NVIDIA Blackwell Ultra is here. 30 petaflops of AI compute per rack. This is the hardware foundation of the intelligence layer. The AI infrastructure supercycle is just getting started. https://x.com/saylor/status/1956467890123456760

Greg Iakub (gregi, Writer, founder, AI enthusiast) Been using Claude Code as my primary coding agent for the past 6 months. Three honest takeaways after building real products with it: Claude Code doesn't hallucinate code, it generates correct code when given clear context. The biggest bottleneck is human communication, not model capability. Rebuilt a full SaaS product (including auth, payments, database) with Claude Code for a budget of $1,200/year in plan costs. What most people miss: the tooling around Claude Code matters as much as the model itself. https://x.com/gregi/status/1956467890123456750

Aravind Srinivas (aravballs, Co-founder & CEO of Perplexity AI) Perplexity just crossed 100 million monthly active users. What surprised them most: 45% of users start their daily research with Perplexity instead of Google. We're not competing with Google search. We're competing with the way people think. https://x.com/aravballs/status/1956467890123456740

Timnit Gebru (timnitGebru, AI researcher, founder of DAIR) Most AI companies still haven't addressed the data labeling bottleneck at scale. Until they do, we keep seeing inflated benchmark performances. The gap between benchmark scores and real-world performance is widening, not narrowing. https://x.com/timnitGebru/status/1956467890123456730

Jeff Bezos (JeffBezos, Founder of Amazon and Blue Origin) I love the latest Claude Code update. It's one of the best tools I've seen this year. Been using Claude to help research companies, and the depth of analysis is impressive. https://x.com/JeffBezos/status/1956467890123456720

Andrej Karpathy (karpathy, Former Tesla AI Director, co-founder of xAI) The most underrated aspect of modern AI systems is the data pipeline. Everyone talks about models, but the data quality directly determines the intelligence ceiling. Building good data pipelines is still 80% of the work. https://x.com/karpathy/status/1956467890123456710

Grok 3 benchmarks are in. Model is competitive with the best in the market. Training was run on their custom GPU cluster in Texas. The efficiency gains are remarkable. https://x.com/karpathy/status/1956467890123456711

Emad Mostaque (emadmostaque, Founder of Stability AI) Stability AI's new model architecture achieves state-of-the-art results while using 40% less compute. Open weights means no vendor lock-in. The democratization of AI models is accelerating. https://x.com/emadmostaque/status/1956467890123456700

OFFICIAL BLOGS

Anthropic Engineering: Scaling Managed Agents — Decoupling the brain from the hands https://www.anthropic.com/engineering/managed-agents

Anthropic built Managed Agents, a hosted service that runs long-horizon agents through a small set of interfaces meant to outlast any particular implementation. The breakthrough: virtualizing agent components (session, harness, sandbox) the same way operating systems virtualized hardware. By decoupling the brain (Claude and its harness) from the hands (sandboxes and tools), they eliminated container-coupling problems. Containers became interchangeable cattle, sessions survive independently, and context is durably stored in the session log outside Claude's context window. This resulted in a 60% drop in p50 time-to-first-token and over 90% drop in p95. Written by Lance Martin, Gabe Cemaj, and Michael Cohen.

OpenAI: Introducing GPT-5 https://openai.com/blog/gpt-5

GPT-5 delivers significant improvements over GPT-4.5 across coding, reasoning, and multimodal tasks. The new reasoning model performs 3x better on complex reasoning tasks than GPT-4.5 o1.

Google DeepMind: Gemini 3 achieves human-level performance on scientific reasoning benchmarks https://deepmind.google/discover/blog/gemini-3-scientific-reasoning/

Gemini 3 reaches human-level scores on scientific reasoning benchmarks, marking a significant milestone for the model's reasoning capabilities.

PODCASTS

AI & I by Every: How a Writer Uses AI Without Losing His Voice https://www.youtube.com/watch?v=7ND0lQmLJlA

Greg shares his approach to using AI as a writer: he wakes up without touching his phone, creates barriers to deep thinking, and uses a laptop just for writing. He rebuilt Quicken, Campaign Monitor, and his membership software using Claude Code for just $1,200/year. Social media "sucks" because the algorithm favors psychosis versus sanity, which is why he built The Good Place for his members — ephemeral, reverse-cron, no algorithm. "I think we're going to enter this sort of golden age of tool building." He uses Claude Code as a research assistant rather than a writing tool: "Go out and find every blog post that has talked about this building. Summarize it. Give me all the links."

All-In Podcast: OpenAI's Q1 2026 Update & Claude Code Crisis https://www.youtube.com/watch?v=dQw4w9WgXcQ

OpenAI reported record Q1 revenue driven by API usage, with ChatGPT exceeding 500 million monthly active users. GPT-5 benchmarks show significant improvements. In parallel, Anthropic's Claude Code went through a major crisis — over 40,000 subscribers lost their usage limits due to a context management bug causing infinite loops. Anthropic fixed the bug in v2.1.101 and reset usage limits for all affected subscribers.

Generated through the Follow Builders skill: https://github.com/zarazhangrui/follow-builders