Michael Bodekaer Jensen

AI tech nerd.

[Michael Bodekaer Jensen @Bodekaer]

@Jason I’m in

[Retro Tech Dreams @RetroTechDreams]

Tribute to Winamp (by @rickgude)

[Michael Bodekaer Jensen @Bodekaer]

@SahilBloom Tip that worked for me: Write an employment contract for yourself, where meaningful free time activities becomes part of your “job” :)

[Michael Bodekaer Jensen @Bodekaer]

@elonmusk @bryan_johnson can’t we fix this by just having everyone live forever? ;)

[Michael Bodekaer Jensen @Bodekaer]

@earthcurated @CrispeThe

[Michael Bodekaer Jensen @Bodekaer]

@pallavmac AGI is here 😂

[Michael Bodekaer Jensen @Bodekaer]

@markvalorian @bennyjohnson @grok why would anyone vote against this? Any public statements about their justification/reasoning?

[Michael Bodekaer Jensen @Bodekaer]

@ForrestPKnight Yes.

[Michael Bodekaer Jensen @Bodekaer]

@francip Yeah just realized the same. If you ask, it just "fakes it" with gray/white squares 😂.
https://t.co/Ok9iHMNaGT helps, but not ideal

[Michael Bodekaer Jensen @Bodekaer]

@djcows @grok how much energy could a plant like this realistically generate? Would it matter compared to the complexity and risk of construction vs alternatives like solar?

[Michael Bodekaer Jensen @Bodekaer]

@elonmusk Had to wait 1 year for delivery 😂

[Mailbird @mailbird]

Custom Avatars in action! Add photos or icons to instantly spot the email accounts that matter most—because managing multiple accounts should be effortless (and a little more fun!). 🎨 #MailbirdforMac

Make your inbox uniquely yours: https://t.co/5GUAgzl09S

[Michael Bodekaer Jensen @Bodekaer]

@Giuliano_Mana Great thread! Very inspiring. Thanks for sharing.

[Elon Musk @elonmusk]

Ship landed precisely on target!

Second of the two objectives achieved.

[SpaceX @SpaceX]

Watch Starship's fifth flight test https://t.co/LVrCnTv797

[Sam Altman @sama]

advanced voice mode rollout starts today! (will be completed over the course of the week)

hope you think it was worth the wait 🥺🫶

[Sam Altman @sama]

The Intelligence Age: https://t.co/vuaBNwp2bD

[Elon Musk @elonmusk]

Day 3 update of the Polaris spacewalk mission

[Jim Fan @DrJimFan]

OpenAI Strawberry (o1) is out! We are finally seeing the paradigm of inference-time scaling popularized and deployed in production. As Sutton said in the Bitter Lesson, there're only 2 techniques that scale indefinitely with compute: learning & search. It's time to shift focus to the latter.

1. You don't need a huge model to perform reasoning. Lots of parameters are dedicated to memorizing facts, in order to perform well in benchmarks like trivia QA. It is possible to factor out reasoning from knowledge, i.e. a small "reasoning core" that knows how to call tools like browser and code verifier. Pre-training compute may be decreased.

2. A huge amount of compute is shifted to serving inference instead of pre/post-training. LLMs are text-based simulators. By rolling out many possible strategies and scenarios in the simulator, the model will eventually converge to good solutions. The process is a well-studied problem like AlphaGo's monte carlo tree search (MCTS).

3. OpenAI must have figured out the inference scaling law a long time ago, which academia is just recently discovering. Two papers came out on Arxiv a week apart last month:

- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling. Brown et al. finds that DeepSeek-Coder increases from 15.9% with one sample to 56% with 250 samples on SWE-Bench, beating Sonnet-3.5.
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters. Snell et al. finds that PaLM 2-S beats a 14x larger model on MATH with test-time search.

4. Productionizing o1 is much harder than nailing the academic benchmarks. For reasoning problems in the wild, how to decide when to stop searching? What's the reward function? Success criterion? When to call tools like code interpreter in the loop? How to factor in the compute cost of those CPU processes? Their research post didn't share much.

5. Strawberry easily becomes a data flywheel. If the answer is correct, the entire search trace becomes a mini dataset of training examples, which contain both positive and negative rewards.

This in turn improves the reasoning core for future versions of GPT, similar to how AlphaGo’s value network — used to evaluate quality of each board position — improves as MCTS generates more and more refined training data.