AI coding

Menlo Ventures partner Matt Murphy told Equity podcast that Anthropic hit a $47B revenue run rate by May 2026, up from $9B in 2025—growth he hasn't seen in 25 years across internet, mobile, or cloud waves. Menlo led Anthropic's $500M Series D at a $4B pre-revenue valuation. Murphy argues the model was never the real moat; Claude Code, MCP, and Claude Skills turned Anthropic into a platform. He also flagged Lovable and Legora as growing even faster, and pushed back on criticism that Anthropic's Mythos launch was more marketing than safety—though the post doesn't detail his counterarguments.

Codeberg members voted to amend the Terms of Use, banning projects that mostly consist of LLM-generated code without human review. The proposal argues such projects have unclear copyright and lack safeguards. The post doesn't define 'mostly' or specify an enforcement timeline.

Fireworks ran ~1,030 real agent tasks comparing Kimi K3 and Fable 5. Head-to-head on SWE: K3 92.4%, Fable 92.6% — a near tie. Under the hood, K3 excels at symbolic math and dev tooling; Fable wins on web and data viz. Routing between the two lifts accuracy to 93% and cuts cost up to 50x on long agent loops. Oracle routing shows K3 can handle 72–96% of tasks, but a practical router still needs an order of magnitude more routing data.

Poolside released Laguna S 2.1 today, a 118B MoE model with 8B active parameters per token and a 1M-token context window. It scores 70.2% on Terminal-Bench 2.1, beating DeepSeek-V4-Pro Max (64.0%) and Inkling (63.8%), and trailing Tencent Hy3 (295B) by only 1.5 points. On DeepSWE long-horizon tasks it hits 40.4% vs DeepSeek-V4-Pro Max's 9.0%. Poolside says training to launch took under nine weeks and published full eval trajectories. The post doesn't disclose training data cutoff or non-coding performance.

Google DeepMind released Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber. 3.6 Flash is the new workhorse—better at coding and multimodal tasks, with up to 17% lower token usage and a cheaper price than 3.5 Flash. 3.5 Flash-Lite targets extreme cost efficiency, and 3.5 Flash Cyber is fine-tuned for finding and fixing security vulnerabilities, available only to governments and trusted partners in a limited pilot. The whole drop is about efficiency, latency, and reliability for customers building AI agents at scale. The real story is what’s absent: the flagship Gemini 3.5 Pro hasn’t been updated since February, while OpenAI shipped GPT-5.5 and started rolling out GPT-5.6, and Anthropic launched Claude Opus 4.8. The post doesn’t explain what’s holding up the Pro line.

Josh Bleecher Snyder now says calling Claude a compiler is a category error — it's better. A compiler only handles source-to-binary decisions, but Claude works vertically across strategy, product, architecture, and machine code. He walks through building exe.dev's distributed DNS server: multiple concurrent agent loops implemented the same design yet made wildly different choices on decisions they never asked about. By answering their questions and reverting bad calls, he slowly converted hard-won knowledge into terse written guidance.

Moonshot AI's Kimi K3 beat Fable and GPT-5.6-Sol on Arena's frontend coding leaderboard and came close on other benchmarks. It's a 2.8T-parameter model with a 1M-token context window and image support; weights will be open-sourced by July 27. Token inefficiency cancels its per-token price advantage: half the cost per token but twice the tokens used. New subscriptions are paused due to GPU shortages. Fable 5 is now a permanent part of Claude Max/Team plans, with Pro users getting a one-time $100 credit. Fable also found a counterexample disproving the 87-year-old Jacobian conjecture. Sierra launched Horizon, outcome-priced long-running agents. NotebookLM rebranded to Gemini Notebook and added Collections.

Anthropic's Cat Wu and Thariq Shihipar shared Claude Code team practices at AI Engineer World's Fair with Simon Willison. Claude Tag, their Slack collaboration tool, now lands 65% of the team's product engineering PRs. They found that stuffing system prompts with examples or 'don't do X' rules hurts output quality on Fable 5 and Opus 4.8, so the Claude Code system prompt shrank by 80%. Thariq noted Fable can edit video to meet their brand team's bar, and the team now treats rewrites as a valid move—the Bun-in-Rust Claude Code already shipped to everyone. The transcript cuts off before detailing what non-engineers do with Claude Tag.

Moonshot's Kimi K3 and Alibaba's Qwen 3.8 launched this week, both near Anthropic Fable 5 in performance and set to release weights publicly. The piece runs the numbers: Anthropic leases data centers and buys electricity, so inference costs scale with usage. Fable 5 costs nearly 3× per completed task vs. competitors. Open models catching up makes a premium-pricing strategy fragile. Anthropic bets on regulation and recursive self-improvement, but its product moat is thin—open-source harness startups are flooding in. The post doesn't spell out a clear countermove.

The author asked Kimi K3 to optimize a hand-tuned Rust function. The model chose a SWAR approach, treating a 64-bit value as eight u8 values, completed the task in about 15 minutes with zero errors, and delivered a 2% speedup. Token cost was roughly $1 via OpenRouter. The author sees this as near-frontier performance.

The UK's AISI found that GLM-5.2 and DeepSeek V4-Pro now trail closed frontier models by only 4–7 months on cyber tasks, down from 6–10 months in 2025. GLM-5.2 matches Claude Opus 4.6 on 70 narrow evals but falls further behind on long-horizon hacking ranges. Kimi released K3, a 2.8T-parameter model that scores near Claude Fable 5 and GPT 5.6 Sol, though the post hints at benchmark overfitting. K3 also wrote a GPU compiler and designed a chip in 48 hours; weights will be released in weeks. DeepMind's Demis Hassabis proposed a FINRA-style US standards body to test frontier models for national security risks, starting with voluntary 30-day pre-release reviews before moving to law.

Cursor redesigned its agent swarm into a tree-shaped planner-worker split and retested it on rebuilding SQLite in Rust from docs. The new swarm beat the old one in every model config: with Grok 4.5 it hit 80% on a held-out SQL test suite in 4 hours, while the old swarm spiraled before hour two. Quality stayed similar across model mixes, but costs varied enormously—the post shows a comparison chart without exact dollar figures. The design isolates context so planners never see implementation details and workers only focus on narrow tasks, preventing drift on long runs.

Approval gates only decide whether a command starts, not what happens after package managers load scripts and spawn child processes. The article walks through a bug-fix task to show how OS isolation (Seatbelt/bubblewrap), credential proxying (Docker Sandboxes), and isolated workspaces each address different risks. No performance numbers or latency figures are disclosed.

Moonshot AI plans to IPO within six months, riding the momentum of its new Kimi K2 model. K2 matches OpenAI o3 and DeepSeek V4 Pro on math and coding benchmarks. The company is valued at about $3 billion, with roughly $150 million in 2025 revenue from Kimi chatbot subscriptions and API fees. The post doesn't specify the listing venue or underwriters. The six-month timeline hinges on market conditions and regulatory approvals—don't bank on it yet.

xAI released the Rust client harness that handles local files, commands, and permissions for Grok Build under Apache-2.0. The Grok model, cloud services, and the official binary build chain remain closed. The repo doesn't accept external PRs. The commit from the earlier upload controversy isn't in the public history, so the current code can't close that case. The real win: you can now pin a public commit, build it yourself, and compare its behavior against the official binary.

The author ran Kimi K3 alongside Claude for coding and couldn't tell them apart on output quality or token usage. K3's API costs $3/$15 per million input/output tokens vs Claude's $10/$50. Subscriptions are even more lopsided: Kimi's $39 coding tier is far more generous, while Claude's $20 plan quietly dropped Fable access because the economics didn't work. The bigger story is US AI policy failure—restricting American models only constrains American customers, while frontier-quality Chinese models like K3 and GLM 5.2 ship without those limits. Semgrep found GLM 5.2 beating Claude on cyber benchmarks precisely because the restricted model declines work the open one just does. The author expects the US to repeat its auto-industry playbook: subsidies and tariffs propping up domestic models that can't compete internationally.

The author tested Claude Fable 5 and GPT-5.6 Sol on an unpublished fiber-network optimization problem, each with three 30-minute runs, comparing plain mode against /goal. Fable 5's plain mean was 32,386—1,875 points lower than Sol's 34,261—and its three plain runs stayed within a 319-point range, showing remarkable consistency. /goal won four of six trials but made both models' means worse: Fable 5 by 759 points, Sol by 868. The feature occasionally gives a small edge but can also cause large regressions. The post also breaks down how /goal differs under the hood: Claude Code uses Haiku as a transcript-only evaluator, while Codex has persisted state and lifecycle tools. Bottom line: Fable 5 is the real story here; /goal is not a safe default.

This piece reframes DSLs as building a smaller executable world for LLMs, not just inventing syntax. The author walks through his own abandoned DSL project and argues that AI has lowered the cost of designing languages, writing parsers, and preparing examples—so teams can build domain boundaries first and decide their lifespan later. The core stack: FastAPI provides atomic actions, DSL describes complete plans, and Context Infrastructure stores past judgments and corrections. The article cites Unmesh Joshi's DSL post and the Tickloom demo, but notes Tickloom is an engineering showcase without cross-model benchmarks proving DSLs improve LLM accuracy. It warns that overly narrow boundaries can exclude correct answers—effect surface and composition rules matter more than Turing completeness.

Cursor's eval lead Nate Schmidt explains on Anthropic's blog how they determined Claude Fable 5 was ready for the hardest 1% of real-world coding problems. The headline number is 72.9% on CursorBench, a significant jump over the prior generation. The post stresses this isn't a generic benchmark grind—it targets long-tail tasks that actually stump developers. The article doesn't disclose the baseline score, test set size, or sample problems, so treat the 72.9% as a directional signal rather than a cross-benchmark comparison point.

Google DeepMind released Gemini 3.5 Flash Cyber, fine-tuned from 3.5 Flash to find, verify and patch vulnerabilities quickly. With multiple calls, it approaches larger models on benchmarks such as CyberGym.