curated · 2026-06-26

The reason to click: the legal strategy just shifted. The NYT is no longer only arguing that OpenAI trained on copyrighted material. They're now saying Microsoft built a purpose-built supercomputer knowing it would be used to generate infringing outputs, and that makes them contributorily liable under the Supreme Court's brand-new Cox ruling. The complaint also drops a sharp detail—Microsoft internally discussed using synthetic data instead of copyrighted material but didn't follow through. That's a tough fact for Microsoft because it suggests they had alternatives and chose not to use them. The post doesn't include Microsoft's specific response yet, so I'd hold off until we see how they counter the 'knowingly' argument.

The numbers are what make this worth a click: GPT-5.5 gave left-leaning answers on ~30 policy issues 80% of the time, with only 3% right-leaning. Gemini 3.1 Pro played it safest at 93% both-sides responses. Grok 4.3 was the only model with a 33% right-leaning share. I'd discount this a bit though. The post doesn't disclose the exact prompts, sample size, or full issue list. There's a big difference between asking "should taxes go up or down" versus "analyze both parties' tax proposals"—and we don't know which approach was used. The more interesting claim isn't about left vs. right. It's that ranking preferences, refusal rules, and default response styles collapse political disagreement into a single moral frame before trade-offs are even shown. That's directionally right, but I'd want to see the raw test design before treating this as settled.

This piece earns a click because it nails why computer use progress has been slow. The current lab bet is that millions of verifiable tasks will get us to AGI. Dwarkesh adds a missing constraint: the domain must also be grindable—you need deterministic, replayable environments where you can run tons of parallel rollouts. His example is sharp: ordering on Etsy is verifiable, but you can't send a thousand agents at Amazon's checkout without getting banned. That's why coding and math race ahead while computer use lags. His proposed fix is AIs learning on the job via in-context learning across very long horizons, rather than relying solely on one-time weight updates. This points toward architectural work on context windows, not just more GPUs. No product names or timelines are disclosed—it's a conceptual piece—but the grindability lens is a useful one to keep in your evaluation toolkit.

This one's worth a look because Ornith-1.0 drops a full family of agent-coding models—9B to 397B—under MIT license, and the 397B's 82.4 on SWE-Bench Verified is genuinely solid. They post-trained on gemma4 and qwen3.5 with RL that jointly optimizes task scaffolding and self-improvement, so the model learns to plan before touching code. The tweet claims it matches or beats Claude Opus 4.8, but the post doesn't include Opus 4.8's numbers on the same benchmarks—I'd discount that until we see a side-by-side. Terminal-Bench 2.1 at 77.5 and ClawEval at 77.1 suggest decent generalization to terminal and agent tasks, but NL2Repo at 48.2 shows natural-language-to-repo-level coding is still a weak spot. What's missing: inference cost and latency. A 397B MoE won't be cheap to run.

The hook here is a concrete date: end of 2026. He Xiaopeng is pointing to DCAS UNR 171 series 02, which covers urban NGP functions and becomes a mandatory EU regulation in six months. That's a real regulatory milestone, not a vague roadmap slide. The second reg, UNR ADS, is still a framework for L3-L5, mainly speeding up Robotaxi approvals. I'd discount the "global" framing a bit—EU mandatory doesn't mean every country follows suit overnight, and local certification still applies. But for Chinese automakers planning overseas expansion, this is a hard signal. He also teased XPeng's VLA and VLM rollout abroad in 2027 with mixed-language voice control, though that part is just a one-liner with no details.

This one's worth opening because of the scale: not a single heavyweight like the NYT, but a coalition of nearly 400 local papers. The complaint says Microsoft and OpenAI "systematically and secretly" scraped articles, stripped copyright info, and used the content to train Copilot and ChatGPT. The publishers' argument is blunt—these AI products built billions in market value on our work, and we got nothing. OpenAI's response is the usual line: publicly available data, fair use. Microsoft hasn't commented. Two things I'm watching. One, the plaintiffs include a former New Jersey attorney general, so they've got political weight. Two, local papers are genuinely struggling, which makes their "death knell for local news" argument emotionally stronger in court than a big publisher's would be. But fair use boundaries for AI training are still unsettled in US courts—this won't resolve quickly.

This caught my eye because it turns 'AI auto-illustration' into a concrete, runnable pipeline: the agent reads the article, decides whether it needs a mood image, diagram, or four-panel comic, generates it, and self-checks with rework if it's not happy. It ships with 31 original characters—15 hand-drawn line-art figures and 16 pun-based meme images—with a default hand-drawn light-color style and five alternative skins including 3D blind-box and black-and-white line art. Setup is just Python 3, works with Claude Code or Codex, and needs an OpenAI-compatible image API key (defaults to GPT-image-2). You can also output prompts only and generate manually. I'd discount this a bit: it's one person's workflow shared as open source, not a polished product. Image quality and rework success depend entirely on the underlying model, and the post doesn't show output samples or rework success rates. The useful bit isn't 'how good the art looks'—it's that the read→decide→generate→self-check chain is wired up end-to-end, with ready-to-use characters and skins. If you're already writing with Claude Code, adding this illustration step costs almost nothing.