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OKF: Why Your Agent's Context Layer Is the Problem, Not Your Retrieval Strategy

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...

GPT-5.6 Sol: Ultra Mode, Three-Tier Pricing, and Why METR Says Its Benchmarks Are Broken

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...

OpenAI's Jalapeño Chip: Nine Months to Custom Silicon and What the 50% Cost Claim Really Means

OpenAI just announced Jalapeño , its first custom inference processor, built in partnership with Broadcom and taped out in just nine months. If the cost numbers hold, this is a structural shift in how OpenAI runs its models, and it eventually affects what builders pay to call the API. What Jalapeño Actually Is Jalapeño is an inference-only ASIC (application-specific integrated circuit). Not a training chip. Inference is what runs every time you call gpt-4o or o3 . That's where the compute costs actually land at scale. The chip is built on TSMC's 3nm process node, the same manufacturing tier Apple uses for its A18 Pro. It's a reticle-sized die, meaning it's about as large as a chip can physically be before yield becomes a serious problem at that node. The package includes one large compute chiplet surrounded by eight HBM (high-bandwidth memory) stacks. HBM is what you need for LLM inference: huge memory bandwidth, physically close to the compute. GPUs do this too, b...

GitHub Copilot Switched to Token Billing in June. Some Teams Saw Their Bills Jump Overnight.

GitHub Copilot switched to usage-based billing on June 1, 2026. If your team didn't notice until the invoice arrived, you're not alone. This is the biggest change to Copilot's pricing model since launch, and the developer community's response was clear: over 900 downvotes and 400 comments on GitHub's own announcement thread in the first week. Here's what actually changed, who got hurt, and what to do before next month's bill lands. What the Old Model Was The previous system used Premium Request Units (PRUs). Your plan came with a fixed monthly allotment. When you burned through it, Copilot didn't cut you off. It quietly fell back to a lighter base model. You kept working. You just didn't know you'd dropped to a less capable model. That was a reasonable trade-off for predictability. That safety net is gone. The New Model: AI Credits Every plan now ships with a monthly AI Credits allowance. One credit costs $0.01. The plan prices didn't...

Apple's Foundation Models Framework Is Now a Model Router. Here's What Changes for Builders.

At WWDC26, Apple made a move that most coverage missed. They didn't just update the Foundation Models framework with new models. They restructured it into something closer to a model abstraction layer, one where your Swift code stays the same whether you're calling an on-device model, Apple's Private Cloud Compute, or a third-party provider like Claude or Gemini. That changes the architecture of iOS AI apps significantly. What Actually Changed The Foundation Models framework has existed since Apple Intelligence launched. But until now, it was essentially one thing: an on-device Apple model you called from Swift, with the privacy and latency benefits that come from never leaving the device. WWDC26 turned that into three distinct tiers accessible through one API: The existing on-device model (fast, private, capability-constrained) A new Private Cloud Compute model (bigger, reasoning-capable, 32K token context window) Third-party models including Claude and Gemini, cal...

Concurrent A/B Tests: How to Know When Interaction Effects Actually Matter

If you've run experimentation at any scale, you've hit this scenario. You've got three tests live simultaneously: one on the hero headline, one on the checkout CTA, one on the product page layout. The checkout CTA test shows a 12% lift. You ship it. The lift evaporates. Post-ship numbers look nothing like the test. Your first instinct is novelty effect. But the real culprit might be that the checkout CTA test was running at the same time as the product page layout test, and users who saw both variants behaved differently than those who saw just one. That's an interaction effect. It's one of the least understood problems in applied experimentation, and it's where a lot of phantom wins actually come from. What an interaction effect actually is In statistics, an interaction happens when the effect of one variable changes depending on the level of another. In A/B testing, it means the combined effect of two experiments running on overlapping user populations is...