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DeepSeek made the 75% discount on V4-Pro permanent in late June. Not a promo extension, not a trial period. They called it an "efficiency gain being passed through." That framing matters. It means the new price floor is structural, not a marketing play designed to flip later. The numbers: $0.435/M input, $0.87/M output, and cache hits at $0.003625/M. For context: GPT-5.5 sits at $5/M input and $30/M output. Claude Fable 5 is $10/M and $50/M. DeepSeek V4-Pro is roughly 34x cheaper per output token than GPT-5.5. At that delta, you're not comparing pricing tiers anymore. You're looking at different economic regimes. What Actually Changed V4-Pro was already a serious model before the cut. It's a 1.6 trillion parameter MoE with 49B active params, a 1M token context window, and MIT-licensed. It scores 80.6% on SWE-bench Verified , the highest open-weights entry, tied with Gemini 3.1 Pro. The price cut didn't change the model. It changed what's economically v...

On June 18, Noam Shazeer posted on X that he was joining OpenAI. His title: lead for AI architecture research, confirmed by OpenAI's chief research officer Mark Chen. If that name doesn't mean anything to you, here's the context that makes it matter. Shazeer is one of eight co-authors of "Attention Is All You Need," the 2017 paper that introduced the Transformer architecture. The architecture every major language model runs on today. GPT-5.5, Claude Fable 5, Gemini 3.5 Flash, Llama, Mistral, all of them. The ideas in that paper are as foundational to modern AI as UNIX was to operating systems. He left Google after that paper, co-founded CharacterAI, and built it into a consumer AI product with enormous scale. In August 2024, Google paid approximately $2.7 billion (structured as a technology license from CharacterAI) to bring Shazeer and a cohort of researchers back into Google DeepMind. His role: VP of engineering and co-lead of Gemini, specifically owning the ...

Google made a specific promise at Google I/O on May 19: Gemini 3.5 Pro would be generally available by June. Sundar Pichai, when pressed on the timeline, said "give us until next month." The audience groaned audibly. They were right to. As of June 29, Gemini 3.5 Pro is still in limited Vertex AI enterprise preview. The public launch has been pushed to July . And in the same week the June deadline slipped, four senior Gemini researchers announced they were leaving for Anthropic . Google's AI coding teams have lost six researchers in five months. That combination is not damning by itself. But it is a signal, and I think it's the more interesting story here. What a Month's Slip Actually Costs A one-month delay sounds minor. It rarely is when you're managing a product roadmap around it. If your team planned feature launches, customer commitments, or integration timelines around Gemini 3.5 Pro hitting GA in June, you just ate a planning hit. The areas Google ...

Obsidian is a note-taking app, but that description undersells it. The thing that makes it different from Notion, Roam, or any of the cloud-based alternatives is this: your vault is just a folder of Markdown files on your computer. No proprietary database, no sync service you're locked into, no account required. Just .md files sitting on disk, organized however you want. That simplicity is also what makes it uniquely useful as a context layer for AI. What Obsidian Actually Is The core features: a Markdown editor with live preview, a graph view that visualizes how your notes connect via backlinks, and a community plugin ecosystem with over 1,000 plugins. It runs on Mac, Windows, Linux, iOS, and Android. Sync is optional and paid, but your vault works completely offline. What you build in Obsidian is a personal knowledge graph. Each note links to others via [[wikilinks]] . Over time, the graph view shows you which ideas are densely connected and which are isolated. It's the...

Every agent project I've built that touches internal data hits the same wall. The agent needs context: what is this BigQuery table, what do the columns mean, how does it join to the orders table, what's "monthly active users" in your org and not the textbook definition. You end up dumping SQL schemas into the system prompt, pointing at Confluence pages, writing a bespoke context builder that assembles fragments before each request. It works, barely, and it doesn't travel. Move to a different team's data, start a new project, and you're rebuilding it from scratch. Google Cloud published a spec on June 12, 2026 that addresses exactly this: the Open Knowledge Format (OKF), v0.1. It formalizes what Andrej Karpathy called the "LLM wiki" into a portable, interoperable format. What OKF Is (and What It Isn't) OKF is not a service or a platform. It's a file format. The spec fits on a single page. Your knowledge base is a directory of markdow...

OpenAI previewed GPT-5.6 on June 26, 2026, in three variants: Sol, Terra, and Luna. Access is currently limited to roughly 20 US government-approved partner organizations, which means most teams cannot run their own tests yet. But there is still a lot worth digging into: a genuinely interesting architecture change with "ultra" mode, and a finding from METR that fundamentally changes how you should read any Sol benchmark score you encounter. Sol, Terra, and Luna: The Three-Tier Model The naming is celestial but the logic is familiar. OpenAI has codified what we have all been doing informally: routing different tasks to different models based on cost and capability. Sol is the flagship. It targets hard problems in coding ( Terminal-Bench 2.1 state of the art at 88.8%), biology (GeneBench v1), and cybersecurity (ExploitBench). Pricing is $5 per million input tokens, $30 output. Terra is the balanced tier, aimed at high-volume business tasks, customer support, document anal...