I like to look at claims like this in case I can improve Ghostty. I grabbed your source and immediately spotted the issue: your read loop overwrites a 64 KiB ring. Everything older is discarded unparsed per tick. You're faster because you're not actually terminal emulating. It means the number isn't a throughput number. In a kitten benchmark run at the rate you're claiming, roughly 6.6 MB arrives per 60 Hz tick and 64 KiB of it gets parsed. You're read(2)-ing 100% of the data and parsing about 1% of it. There are other minor issues (a small fixed scrollback in a preallocated ring buffer, no grapheme clustering, throwing away data like OSC 8 URIs, etc.), but the major one is simply that you're discarding ~99% of the input. For plain text, e.g. your cat benchmark, the final screen ends up looking right, but the scrollback is missing 99% of the file and each retained chunk starts mid-line. As soon as there are escape sequences in the dropped spans, your emulator is fully out of sync and invalid: a cut inside a CSI prints the parameters as text, and any SGR, mode, title, or alt-screen change in the gap is just... gone. FYI, this is why I don't like when people point at refterm as a terminal benchmark. refterm implements a TINY subset of what a terminal emulator must support, it's closer to a fast file reader, not a terminal emulator. I also don't mean any shade by this. I like to look at technology objectively to try to learn something and improve my own craft and work. So I'm sharing what I found. I modified your code very slightly to parse on every full buffer (rather than per tick). And the results are attached. Both use main commit in each repo at the time of this post. Note the graph is in log scale, the discrepancy between performance for things like Kitty images is measured in GB/s.

Anthropic is now recommending that companies REMOVE unnecessary subagents from their AI systems. This is a pretty significant shift in agent architecture, with their team finding that a single agent equipped with skills can frequently outperform complex multi-agent systems, and one customer seeing roughly 2x faster execution. The emerging rules of thumb: → Skills: Teach the agent HOW to do something, including procedures, expertise, and instructions. → Tools / MCP: Give the agent the ability to DO things through APIs, actions, and live data. → Subagents: Use for parallel work, isolated context, or tasks a single agent struggles with. The interesting part is progressive disclosure. Instead of spawning specialized agents or stuffing everything into a giant prompt, one agent discovers and loads skills and tools as needed. Their advice is to fold existing subagents into the main loop, turn their instructions into skills, expose their actions as tools, and run evals to see if performance holds up. As models have gotten smarter, improving agent architecture is becoming less about what you can add and more about what you can delete. claude.com/resources/article…

despite the unfortunate timing, we take disclosures seriously at @typesafeai at approximately 6:49PM this Friday evening, we became aware of a massive unauthorized leak of ALL customer noul outputs in effort to maximize transparency, here is the link: gist.github.com/diogo149/810…

Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERN’s record by a factor of 10. Last week, we enriched isotopes in the world’s most powerful plasma centrifuge, beating the Soviet record that stood for 40 years. Now, we’re building a fusion reactor in San Francisco to achieve record power density.

I have discovered the the BEST way to get 3D modeling results from LLMs The app LiDAR scans the room while taking hundreds of high quality photos. Then a workflow + skill I've created models out every item recursively checking the model against the photo at the exact spot + angle where the photo was captured The result is a a dimensionally accurate and high quality model of your room All in my latest video on how to do it piped.video/watch?v=JGQCcCzu…

Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.

The highest value thing you can do with your agent is buy a domain and host your own personal website. Use it to build track your finances, fitness, home climate, API usage, etc AI agents are great at building and maintaining websites

we’re actually doomed

5/ So what happened? Market Maker 1 had $500,000 to launch with. It put about $5,200 into the pool, 1% of its cash. The pool opened with under 30,000 coins, 0.003% of the supply. No one has explained why. Every expert we've talked to is baffled.

Proof that Dots is literally a Codex thread and NOTHING special. Accidentally found Dot's backend thread and it's just a cloud codex thread. Nice engineering OpenAI.

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Wtf Muse. It's tracking like this and unprompted callouts that users can never come around to trusting Meta with their data.

Steve Jobs would fire the entire company and replace them with AI if he was alive to see this