benjismith

Knowledge cannot be "stolen"

I think this is just another part of the growing pains of working with machine intelligence that we have to endure.

Much like we previously had to cope with "hallucinations" as an issue.

If the ultimate goal of AI is to develop general intelligence, the first big objective is: thinking systematically. And the road toward systematic thinking right now is mainly coding, mathematics, and other "verifiable reward" domains.

Claude doesn't have a separate mind for "coding" and "writing". Claude has tokens, and tokens can be assembled in various productive structures, mainly optimized right now for systematic reasoning. Also, a token isn't just a chunk of text. A token is like a little neural-network subroutine that fulfills a function. The conversion of a token into a piece of text only happens on the output side...

When the model finds token sequences that lead toward better verifiable outcomes, it leans hard into those token sequences, and uses them as an essential component of its thought process. "Load bearing" is load-bearing. "Verify, rather than assume" is a mantra that produces good results, so it gets repeated over and over again.

It's super-interesting that this particular moment, where the idea of "Claudish" has become a full-fledged meme, coincides with such astonishing progress in coding and math. My wife says when she uses Claude, that it feels to her exactly like talking to an autistic Engineer.

Not a coincidence, I think :)

My feeling is that the next big era of machine intelligence will require more lateral-thinking and creativity, and hopefully then the models "writing" will be more pleasant to read.

Ahhh okay. They've definitely had some growing pains. By "fall from grace", I assumed you meant some sort of ethical/trust failure.

What do you mean by "Github's fall from grace"? What happened?

I've been using the classic word2vec implementation for many years to produce text embeddings for a variety of workloads.

It was written by Tomas Mikolov at Google in 2013 and written in C:

https://code.google.com/archive/p/word2vec/

But with Mojo 1.0 finally officially released this week, I wanted to see if me and my buddy Claude could improve on both the performance and clarity of the implementation.

This new implementation benchmarks at 3.3x faster than the classic C implementation (single-threaded) and 1.8x faster (with 8 threads). This was measured on my M2 Pro MBP from 2023, where I'm fully saturating the memory bandwidth, so more recent chipsets with faster memory buses might have even better relative performance.

I'm interested in getting benchmarks from users on more recent M series chips, and users on other platforms as well! Try it out and submit a PR with your measurements.

But the real win is: clarity. In this implementation, I wanted the code to be readable and well-commented, so that it acts as a teaching tool for implementing fast code in Mojo as well as understanding how the machine learning algorithm works.

It's been a while (15+ years) since I was in that game... but back then, it was $60k for a Phase I and $200k for a Phase II. Phase III and beyond was open-ended.

You don't really make money on Phase I projects, but that's how you get the ball rolling on future work, which can be very lucrative.

I used to work for a company (~50 people) whose entire business was based on the SBIR pipeline. We did a lot of super-interesting work!

Here's my advice:

1) Your proposal needs to be completely solid and well-structured:

- Describe the problem. Put it into a Defense/Intel context. Talk about the needs of the warfighter.

- Do a literature review of the field, and explain what the state-of-the-art looks like today.

- Explain what previous approaches to the problem have been attempted in the past.

- Demonstrate why those approaches are flawed.

- Describe your novel approach.

- Explain why your approach will succeed where others have failed.

- Talk about what you'll deliver in your Phase I deliverable, so that you can demonstrate proof-of-concept.

- Talk about how your eventual Phase II will put your proof-of-concept into a real-world scenario, and offer at least a glimpse of how your Phase III+ will commercialize.

- Talk about your team. Why are you uniquely capable of solving this problem?

- Talk about your budget. How will you spend the money toward satisfaction of the deliverables (salaries, subcontractors, equipment and supplies, etc)

2) As soon as you know you're interested in a topic, send an email to the Principal Investigator, telling them you'd like to meet with them to talk about the topic. Before the phone call, research the PI's history with this topic. Also, lookup the archive of SBIR topics, to see if this person has been a PI on similar topics in the past.

When you meet with them, ask clarifying questions that demonstrate you know the domain. Try to get as much specificity as you can... Ask them what their success criteria look like. See if you can get them excited!

Most importantly, by the time you submit, the PI should already know your name and to expect your submission.

3) If you're not already a recognized expert, with published academic papers on the topic, that's okay! But you'll improve your chances of winning a grant if you hire a known researcher as an advisor. For example, I've hired a Computer Science professor to supervise one of their own grad students, while doing paid work on a SBIR project. So the professor's credentials and the grad student's previous publications also became part of the SBIR proposal.

Anyhow, good luck!

It makes perfect sense.

Apple's philosophy is that new APIs need some time to stabilize before they can be baked-in as a commitment to third-party developers.

So new APIs are almost always first-party only. Apple designs the API and becomes the first consumer of it. This experience of dogfooding their own APIs lets them iterate and learn without breaking compatibility with third-party developers consuming the API.

Only after an API has been hardened in this way does it become eligible for third-party consumption, where Apple can promise to document and support those APIs publicly.

It makes sense then, that if the DMA mandates equal access to new APIs for third-parties, then Apple will just disable new first-party APIs in the region until they've gotten their bake-in period elsewhere in the world. Sorry, EU!

I thought this pull-quote was interesting:

"Interoperability only works when it is built into the platform from the start"

-- Lucas Lasota, FSFE Legal Programme Manager

To my mind, this is almost exactly opposite of true. Most new capabilities need to be incubated in private first, so that the APIs can get real-world usage and have a chance to evolve into a stable state before they become public interoperability promises.

This is true, but I also think the input context isn't the only function of those tokens...

As those tokens flow through the QKV transforms, on 96 consecutive layers, they become the canvas where all the activations happen. Even in cases where it's possible to communicate some detail in the absolute minimum number of tokens, I think excess brevity can still limit the intelligence of the agent, because it starves their cognitive budget for solving the problem.

I always talk to my agents in highly precise language, but I let A LOT of my personality come through at the same time. I talk them like a really good teammate, who has a deep intuition for the problem and knows me personally well enough to talk with me in rich abstractions and metaphors, while still having an absolutely rock-solid command of the technical details.

But I do think this kind of caveman talk might be very handy in a lot of situations where the agent is doing simple obvious things and you just want to save tokens. Very cool!

Product Engineer (full stack, design + ux)

Former Founder / Principal Engineer of https://shaxpir.com

Location: Portland, OR

Remote: Yes

Willing to Relocate: Yes

Technologies: Java, AWS, TypeScript, Elasticsearch, Realtime Collab, LLM APIs, Claude Code, etc

Résumé/CV: https://www.linkedin.com/in/benjismith/

Email: [email protected]

> show LoRA on a 400B model, or full fine-tuning on a 70B

Yeah, that's what I wanted to see too.

I think the biggest injury to the hiring of junior devs happened after COVID made remote-work ubiquitous. It's a lot harder for a junior dev to get real mentorship, including the ambient kind of mentorship-by-osmosis, when everyone works alone in a sad dark room in their basement, rather than in an office with their peers and mentors.

The advent of agentic coding is probably punch #2 in the one-two punch against juniors, but it's an extension of a pattern that's been unfolding for probably 5+ years now.

I read it. I also searched the page for the word "Opus" and it didn't appear anywhere. The word "Sonnet" appears, but only once.

There's also "GPT-4.1 or GPT-5", but that's not what my question implied, which was that it's weird to offer Sonnet but not Opus.

Sonnet only?

A similar kind of question about "understanding" is asking whether a house cat understands the physics of leaping up onto a countertop. When you see the cat preparing to jump, it take a moment and gazes upward to its target. Then it wiggles its rump, shifts its tail, and springs up into the air.

Do you think there are components of the cat's brain that calculate forces and trajectories, incorporating the gravitational constant and the cat's static mass?

Probably not.

So, does a cat "understand" the physics of jumping?

The cat's knowledge about jumping comes from trial and error, and their brain builds a neural network that encodes the important details about successful and unsuccessful jumping parameters. Even if the cat has no direct cognitive access to those parameters.

So the cat can "understand" jumping without having a "meta-understanding" about their understanding. When a cat "thinks" about jumping, and prepares to leap, they aren't rehearsing their understanding of the physics, but repeating the ritual that has historically lead them to perform successful jumps in the past.

I think the theory of mind of an LLM is like that. In my interactions with LLMs, I think "thinking" is a reasonable word to describe what they're doing. And I don't think it will be very long before I'd also use the word "consciousness" to describe the architecture of their thought processes.

Same for me :)

Is there way to get "speech marks" alongside the generated audio?

FYI, Speech marks provide millisecond timestamp for each word in a generated audio file/stream (and a start/end index into your original source string), as a stream of JSONL objects, like this:

{"time":6,"type":"word","start":0,"end":5,"value":"Hello"}

{"time":732,"type":"word","start":7,"end":11,"value":"it's"}

{"time":932,"type":"word","start":12,"end":16,"value":"nice"}

{"time":1193,"type":"word","start":17,"end":19,"value":"to"}

{"time":1280,"type":"word","start":20,"end":23,"value":"see"}

{"time":1473,"type":"word","start":24,"end":27,"value":"you"}

{"time":1577,"type":"word","start":28,"end":33,"value":"today"}

AWS uses these speech marks (with variants for "sentence", "word", "viseme", or "ssml") in their Polly TTS service...

The sentence or word marks are useful for highlighting text as the TTS reads aloud, while the "viseme" marks are useful for doing lip-sync on a facial model.

https://docs.aws.amazon.com/polly/latest/dg/output.html

If I'm reading the pricing correctly, these models are SIGNIFICANTLY cheaper than ElevenLabs.

https://platform.openai.com/docs/pricing

If these are the "gpt-4o-mini-tts" models, and if the pricing estimate of "$0.015 per minute" of audio is correct, then these prices 85% cheaper than those of ElevenLabs.

https://elevenlabs.io/pricing

With ElevenLabs, if I choose their most cost-effectuve "Business" plan for $1100 per month (with annual billing of $13,200, a savings of 17% over monthly billing), then I get 11,000 minutes TTS, and each minute is billed at 10 cents.

With OpenAI, I could get 11,000 minutes of TTS for $165.

Somebody check my math... Is this right?

Nope. They needed the maximum amount of thrust from those boosters in order to propel the spacecraft toward Jupiter, so they couldn't save enough fuel for the boosters to land themselves. This was the 6th flight of these boosters, so we thank them for their service!

Awesome, I'm been following Seph's work for many years! Always thoughtful and well-executed. Probably the most prolific and insightful engineer in the "collaborative text editing" universe.

I use ShareDB every day, which originated from Seph's excellent work on OT algorithms. Good stuff!

It doesn't sound like they "failed" any actual safety test, but rather that they rushed their safety tests, thereby "failing" (in the eyes of many people) to conduct sufficiently rigorous tests.

Now that the 4o model have been out in the wild for 2 months, have there been any claims of serious safety failures? The article doesn't seem to imply any such thing.

Nice! Thank you for the links!

This reminds me of the amazing molecular animations of Drew Berry, which he showed in this TED talk:

https://youtu.be/WFCvkkDSfIU?si=JNe06VS8TjIrHpqh

Which was 12 years ago! After watching that video, I had a much greater appreciation for how our bodies are made up of trillions of tiny protein machines. Fascinating stuff!!