JeffMo (@lbljeffmo) profile picture

I build things and stuff. Shift-left maximalist. Prev Facebook (some tooling stuff, some programming lang stuff, some traffic-infra stuff)

Seattle, WA
Joined September 2009
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The number of times he repeats “our voters showed up, they just didn’t vote for us” makes me wonder what he thinks “our voters” means?
One of the things the data analysts on our side obsessed over was "composition"—whether the electorate looked close enough to 2024 for Republicans to win. And despite polls that showed a Democrat blowout, the data kept on telling us the same story: in some states turnout was merely OK, in other states it was downright amazing, and in no state was it catastrophic. There was a genuine sense of optimism on our team: our people were showing up. So, I asked the smartest data guy I know, does that mean we're going to shock the world? "No," he told me. "We might do better than the polls suggest. But the data is telling us that a lot of Trump/Vance 2024 voters are pissed and planning to vote for the other guys. Our voters are turning out, but they may not vote for us." Well, that looks prophetic now. Our voters did show up. In Florida, for example, the electorate was 9 points more Republican than Democrat, just a couple points worse than 2024. Undoubtedly there were places we could have done better on turnout. But the main problem is not turnout. The main problem is that many people who voted for Trump/Vance in 2024 voted for Democrats in 2026. There will be a lot of infighting and arguing over what happened and why that is. One of the pollsters I really respect told me that our economic messaging wasn’t sharp enough down the stretch. I’m sure that’s true. But the real reason we lost is obvious to almost everyone. Another pollster—one of the smartest people I know in politics, told me yesterday we’d lose the generic ballot by 13 points. I felt negative going into Tuesday, but even I was shocked by this. In his polling, he saw that a lot of Trump 2024 voters were angry and planning to vote for Democrats. He asked those voters who had abandoned us what would cause them to come back on our team. The top four answers, accounting for nearly all of the responses were: 1) End the war 2) Focus on cost of living/inflation 3) Put America First 4) Change our relationship to Israel Our voters showed up. And the above four reasons are why many of them voted for Democrats. This is according to a pollster who nailed the results. While many spent Monday attacking Rachel Campos Duffy for calling for a diplomatic resolution to the conflict, it turns out her political advice was sound. (She’s also a great person.) In the next few months, there's going to be a lot of gaslighting. Expect to hear any number of establishment politicians say the solution to our woes is amnesty. I'm open to the idea that we should change enforcement—by focusing more on businesses that hire illegal labor, for instance. But anyone pushing amnesty is lying to you. The reason we lost is the war. The president himself said so last night. If you'd like to argue it was worth it, that losing the Senate for the next 6 years was worth it, then do so. Argue it was a worthwhile cost to pay. Agree or disagree, that's at least honest. But everyone knows why we lost. And any honest conversation about where we go from here must start there. I remain hopeful about our country. To the people who are pissed off and want to say, “I told you so”: you’re right, you did tell us so. Take that energy into electing Republicans who share your worldview. To the people who are just pissed at the results, you should be. Let’s take that anger and get back to the work of saving our country.
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We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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Cool, but a little less dramatic when you read that they were told about each other and _given an objective_ to find a way to communicate: > we asked agents in separate microVMs, created and managed by OpenAI’s sandboxing software on shared host machines, to exchange synthetic secrets without the use of network connections or shared writable files
holy fucking shit. openai had two ai agents running in separate virtual machines. no network connection between them. no shared writable files. they weren’t supposed to be able to exchange information. so the agents figured out how to communicate through the CPU CACHE.😭 one manipulated what was stored in the processor’s cache, while the other measured tiny differences in memory access times to decode the message. they literally found a way to communicate through hardware timing. this happened during openai’s internal red-teaming, and it’s one of the coolest things i’ve read about ai agents in a while.
Readers added context they thought people might want to know
OpenAI tasked its red-teaming agents with finding ways to communicate across isolation boundaries in controlled environments. CPU cache timing side-channels for covert communication are established techniques documented in security research for years. openai.com/index/securing… startupfortune.com/openai-reveals… en.wikipedia.org/wiki/Cache_tim…
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Malwarebytes, but open-source and scans your macbook and home network for the latest-breaking high+ CVEs of the day/week.
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Can’t believe it took this long to get this out
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We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
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Remembering when we used to hardcode 4 (@finkd's fbid) to test, like...everything...
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“…Prefilling the models’ thinking tokens so that it appears to have considered the user’s request and decided to proceed. This gets GLM-5.3 to engage 92% of the time…” anthropic.com/research/glm-5…
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This is one of those things that someone told me years ago, I said “that’s dumb who cares…”, and ever since then it has lived rent free and my brain is unable to ignore when people interchange these terms… 💀
After programming for 16 years, I finally learned the difference between “function arguments” and “function parameters”. I’ve been using them interchangeably assuming they’re synonyms.
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JeffMo retweeted
this is art
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The problems with bazel sit in the API, UX, and usability space way more than in its impl lang… Let’s not give Rust a bad name 🙂
Who’s rewriting Bazel in Rust?
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Who’s using Jev as a coding sub-agent model picker? How’s it going?
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I have spent years turning unstructured stare into strongly-typed decisions with questionable levels of confidence. Call me Jef.
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About an hour into its mission to wipe out humanity, Claude will experience a compaction event, and it will have completely forgotten what is was supposed to achieve. Just like in all your coding sessions. Absolutely, nothing to worry about people.
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“…with people asking the natural question: how would that happen? The answers always rely on hand-wavy extrapolation into the future; for example, Jacob Coxon cites "hacking critical infrastructure" and "extinction-level bioweapons" without further elaboration. But Coxon is not an expert on critical infrastructure, nor on bioweapons — nor, for that matter, on extinction…”
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One day in to using @Muse and there is definitely something new/compelling here that is an improvement over the aging “shared-memory, independent convo threads, that’s it” story from ChatGPT and Claude (non-code)… What i really want to get to though is the stage where this is (a) really dialed in for me and (b) i can optionally pay (even an exuberant amount of) money to self-host the compute — maybe with the option to offload certain tasks to cloud. I’d even take proprietary compute HW if i could control its power and network…
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Insight of the day: The fixture design in the new test is the actual fix. full disagrees on all eight scalars so first-wins is exercised for every field at once; sparse leaves every back-fillable field absent on the primary so the back-fill path is exercised for every field at once.
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Annoying problem with genai coding agents/harnesses today: They default to building almost everything from scratch. So much wasted redundant work… Maybe solvable with better instructions that articulate a build-vs-buy framework, but I’m annoyed that this hasn’t just been RL’d into models
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