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Gemini 3.7 Flash is live! - Low latency and lower token costs by 50% while advancing reasoning for coding and agentic loops. - Software Engineering Performance (DeepSWE v1.1): 37.0% ➔ 65.3% - Enterprise Automation (AutomationBench): 13.4% ➔ 30.4% Available now via our APIs, @GoogleAIStudio and @antigravity!

🧩 DeepSeek Harness v0.1 is now available in Developer Preview! 🔹 We’re opening it up to developers building agent harnesses worldwide and open-sourcing the codebase in MIT license. 🔹 Powered by the Cordis meta-framework, DeepSeek Harness is an agent harness built around one core idea: Everything is a plugin. Models, tools, skills, sessions, sandboxes, filesystems, loops, orchestration, and UI are ALL implemented as plugins, and can be mixed, matched, replaced, and extended. Try it now!

Is the AI boom facing its first major credit market warning sign? ⚠️📉 NVIDIA’s 5-year Credit Default Swaps (CDS) surged to a record high of 82 basis points as market concerns mount over massive $750B+ AI infrastructure commitments and vendor financing loops. The latest KuCoin blog breaks down what this credit risk spike means for tech equities and the crypto market: 📉 Credit Default Swaps Surge: Five-year CDS on NVIDIA debt hit a record high of 82 bps—its largest single-day jump—as credit markets aggressively reprice contingent liabilities. 💼 The $750B AI Web: Massive deals including a $500B+ partnership with SK Group and a potential $250B financing backstop for OpenAI are raising concerns over balance sheet complexity. 🔄 Circular Financing Fears: Credit markets are questioning vendor financing feedback loops, where chip demand is partially supported by the supplier's own capital commitments. ⚡ Crypto Market Spillover: As high-beta risk assets, digital assets continue to show tight correlation with technology risk sentiment, reacting directly to credit market signals from the AI complex. Are we seeing the early warning signs of an AI capital bubble, or is this just near-term noise? Read the full analysis here:

Software quality now depends on the constraints you set around your agents. When humans manually wrote most of the code we could look at the code itself for signs of quality. Is it clean? Is it thoughtful? Is it fast? Can another engineer understand it? Does it have tests? Agents can now generate more code than people can read. When code generation scales beyond review, quality - checks for one or more of correctness, maintainability, security, performance etc - increasingly has to live somewhere else. It moves into the harness, environment and operating system around the agent. This can be the tests and deterministic checks that decide what the system is allowed to do (amongst others). Your constraints are what may eventually enable loops of agents to deliver production software reliably. They can include unit tests, property tests, acceptance tests, mutation testing and quality metrics. This back-pressure lets the system resist bad work before it becomes somebody elses problem. Set your constraints. They decide whether the code your agents generate is good enough to ship.

holy opus 5... 🤯 At first I was VERY disapointed, the model is ridiculously slow and the output was not impressive. i was using it WRONG. fable 5 can take a shit prompt and give you gold, but it will also burn all your tokens. opus is the opposite. use loops, graphs, reviewers, orchestrators, and agents working in parallel. ( this subway surfers game took over 3 hours ) Your output will be 10x better. The crazy part is, even with all these agents working together I have PLENTY of usage left on my first claude plan. The skill ceiling is insanely high, if you put time into a good agentic engineering strategy you can achieve BETTER output than fable. Opus 5 is a true daily driver.

Standard validation loops take too long. See how YouTube engineers used AI Studio to mirror the platform, safely testing AI prototypes against live data at light speed on this episode of Emergent →

teaching people to build agent loops right now is the new "here's how to set up a cron job" the primitive is already boring. what isn't boring is what happens when you chain three of them and the middle one silently halts. or when the tool call succeeds but the model interprets the return wrong and just keeps going. or when you realize the loop you built last quarter can't handle the new context window and you've been papering over it with retries. that's the actual curriculum. not "here's how to make an agent call a tool." that part is a tuesday afternoon and a docs tab. the part nobody's really clocking: degradation patterns. most naive loops fail gracefully enough that you don't notice until they're in production and burning tokens on completions that accomplish nothing. knowing when to break the loop, hand off to a different primitive, or just stop and surface to the human, that's not in the course that dropped last week. agent loop as infrastructure means it's load-bearing. you don't celebrate load-bearing. you make sure it doesn't fail silently. what replaces the naive loop isn't a smarter loop. it's usually a simpler one with a harder exit condition.

When you throw a paper airplane on Earth the wings create a lift that pulls it up, but gravity pulls it down; this mix makes it glide forward and down. In microgravity, the wings still create lift, but with nothing to pull the nose down, the lift force causes the plane to turn. If there were enough room, the airplane would fly in loops until it ran out of speed and drifted.