Claude Code: The Six-Reaction Origin Story It started as a side project that got six reactions on Slack. A year later, it's writing the majority of Anthropic's product code. Lamis Mukta from Anthropic tells the origin story of Claude Code, and why the "worst" launches sometimes become the biggest ones. Watch the full episode at tessl.co/00r or listen wherever you get your podcasts.

The most expensive model in the benchmark wasn't the best value. Rob Willoughby and Simon Maple ( @sjmaple ) evaluated 19 model configurations on real agentic tasks and found that DeepSeek V4 Flash scored 82.3 while costing just $0.0236 per task. Claude Haiku 4.5 scored 82.9 at roughly four times the cost, while DeepSeek V4 Pro scored 85.3 at nearly eight times the cost. The interesting part isn't that Flash beat stronger models. It didn't. The interesting part is how little quality was gained for how much additional spend. That becomes a very different conversation once you're running agents at scale. A model that looks marginally better on a benchmark can end up costing dramatically more over the course of a year, especially when agent workloads start growing. The benchmark also surfaced something that many teams probably aren't measuring closely enough. The biggest performance jump didn't come from switching models. It came from adding the right skill. DeepSeek V4 Flash moved from 64.1 to 82.3 with skill context applied, which raises an uncomfortable question about how much of agent performance is actually model selection versus everything built around the model. The full breakdown is worth reading, particularly the sections on points-per-dollar, turn counts, and why the cheapest model in the benchmark ended up being one of the most interesting. Read the full blog here: tessl.io/blog/same-quality-a…

Ryan Lopopolo tracked PR throughput on his OpenAI team from 3.5 per engineer per week up to 70 — not through adding headcount, but through iterating on the model and the harness together. Every revision of GPT-5 from 5.2 onward compounded on the last, and this clip shows exactly what that felt like from inside the team. Watch the full episode at youtu.be/MFQIKbr1IEo or listen wherever you get your podcasts. #AI #agenticcoding #claudecode #codex #AIskills

Developers using AI tools are creating and merging twice as many pull requests — but AI-generated PRs have a 60/40 merge rate compared to 80/20 for humans. That gap reveals something important about how agents are actually being used in the wild: probing, experimenting, spawning throwaway work. Jellyfish's Nick Arcolano breaks down what the data actually says. Watch the full episode at youtu.be/GbHfzFcIa0o or listen wherever you get your podcasts #AI #agenticcoding #claudecode #codex #AIskills

AI Native DevCon is live. After an opening keynote from Simon Maple ( @sjmaple ), Guy Podjarny ( @guypod ) has just taken the stage with "Skills are the New Code". As software development shifts from writing instructions to defining intent, skills, specs, and context are becoming the core building blocks of modern engineering. If you're building AI-native systems, this is exactly the conversation happening right now. Can't join us in London? Watch live from anywhere: youtube.com/watch?v=akZ85mG5…

Your eval leaderboard can change completely depending on which model grades the answers. Simon Maple ( @sjmaple ) reran the same benchmark suite using three different LLM judges: Sonnet, GPT-5.5, and Opus-4-7. Nothing else changed. The tasks, rubrics, scenarios, and model outputs were identical. The scores were not. One model moved by 47 points on a single skill depending on the judge. gpt-5.3 ranked near the top under Sonnet, then dropped sharply under GPT-5.5. Opus consistently scored itself higher than the other judges did. GPT-5.5 turned out to be dramatically stricter overall, averaging almost 7 points lower than Sonnet across the benchmark. What makes this especially interesting is that the instability wasn’t evenly distributed. Tasks with concrete pass/fail conditions stayed relatively consistent across judges. But as soon as the rubric involved interpretation, structure, writing quality, or “best practices”, the variance widened fast. Two judges could look at the exact same output and disagree by double digits on whether the model had actually solved the task properly or just approximated it convincingly. That has pretty major implications for how people read benchmark charts right now. A lot of public evals are presented as if the number is objective, when in reality the scoring model itself is shaping the outcome. In some cases, the judge preference is large enough to reorder the leaderboard entirely. The interesting exception was Opus. It stayed in first place regardless of which model acted as the judge. Everything below it shifted around. Read the full breakdown here: tessl.io/blog/your-benchmark…

The problem is not that AI has not been trained... the problem is that AI has been trained A LOT. @venkat_s shares his thoughts on "Accelerated Inference". Watch the full episode at youtu.be/C0OeWkbhiL8 or wherever you get your podcasts. #AI #agenticcoding #claudecode #codex #AIskills

We audit packages. We audit infra. We don’t audit the instructions shaping our AI agents. That’s the problem tessl-audit is tackling. In a new post, Simon Maple ( @sjmaple ) introduces an open-source CLI that scans the skills and plugins loaded into your agent’s context for security findings, quality issues, and actual task uplift. Because agent skills are not just “extra context”. They directly influence how models reason, which patterns they follow, and what they decide to do. The workflow is intentionally simple: npx tessl-audit The tool reads your tessl.json, fetches registry data for installed skills, and produces a posture report showing risky plugins, weak guidance, and skills that have never been evaluated against real tasks. What makes this interesting is how familiar the problem suddenly feels. AI agents are starting to develop their own dependency graphs, except the dependencies are prompts, policies, evals, and reasoning layers instead of libraries. And unlike broken code, bad context often fails quietly. The post also walks through optimizing low-quality skills, generating eval scenarios, and measuring whether a plugin genuinely improves agent performance before trusting it in production. Read the full post here: tessl.io/blog/stop-trusting-…