Ken_dev

@CodeToTheMoons

Nov 7

Codez

@0xCodez

Nov 5

Andrej Karpathy:

“A 1B model can be much more effective than most current LLMs.

90% of current models waste a ton of capacity and money on remembering stuff that doesn’t matter.”

here is his 5 core thesis about Small LLM’s:

1. the intelligence was never the knowledge. Strip keep the reasoning. What’s left is the cognitive core, and it’s small.

2. the core might fit in ~1B params. Maybe less. “Plenty of room at the bottom.” We’ve been paying for a library when we needed a brain.

3. size stopped being the bottleneck. What makes an agent work is the tools it can call and how tight the feedback loop is, not parameter count.

4. intelligence lives in the system, not the weights. A raw LLM is a CPU with no OS. Small core + good harness > giant model alone.

5. you can own it. A model on your hardware is one no one can rate-limit, price-hike, or deprecate on you.

in 12 months the gap between teams shipping on-device and teams paying per-token frontier bills won’t be capability.

TwIL an 8.8B model at 90%+ of frontier isn’t a benchmark flex.

It’s the thesis Karpathy’s been repeating all year, made real.

study more on this below ↓

Ken_dev

@CodeToTheMoons

Nov 4

Proziam

@Proziam

Nov 4

How can @CodeToTheMoons recommend railway after everything?

Is this a joke? Not only did they leak customer environment variables but they also had this whole incident with @joshmanders like five minutes ago

youtube.com/watch?v=YfahrT…

Ken_dev

@CodeToTheMoons

Oct 23

Artificial Analysis

@ArtificialAnlys

Oct 23

Kimi K3.1 Pro launches at 4th on the Artificial Analysis Intelligence Index scoring 55, making large gains in agentic performance; it will be the leading open-weights model once its weights are published

@Kimi_Moonshot's new reasoning model gains 11 points over Kimi K3 (max, ude Opus 5.5 (max with fallback, 58), Claude Sonnet 5.5 (max with fallback, 56), and Muse Wonder Full (max, 56) on the Intelligence Index.

Once the weights for K3.1 Pro are released it will be the highest-scoring open weights model and the gap between the leading open weights and proprietary models on the Intelligence Index will narrow from 12 points to 3.

Key results:
➤ Large gains in agentic performance: Kimi K3.1 Pro scores 3rd on GDPval-AA with 1,846 Elo, up 312 Elo from Kimi K3, behind only Claude Opus 5.5 (1,866 Elo) and Muse Wonder Full (1,857 Elo). Kimi K3.1 Pro also achieves the highest score of any model on Terminal-Bench 4.0. K3.1 Pro scores 67%, up from 13% for Kimi K3 and ahead of Claude Sonnet 5.5 (64%). AutomationBench-AA, which tests agentic SaaS workflows, rises from 58% to 73%.

➤ Gains in other evals are more moderate: Humanity's Last Exam rises from 47% to 52%, CritPt from 23% to 25%, and SciCode from 59% to 61%. AA-Omniscience Index rises from 20 to 30, as the hallucination rate falls from 53% to 44% and accuracy rises from 48% to 51%.

➤ Cost per Task roughly equal to Kimi K3 at $1.98 per Task: K3.1 Pro uses 55K output tokens per Intelligence Index task, 14% more than Kimi K3 at 48K, while its first-party token prices are 33% lower.

Additional model details:
➤ Context window: 1M tokens.
➤ Size: 2.8T total, 104B active parameters
➤ Input modalities: Text and image
➤ Weights and license: Weights currently not published, Moonshot is expected to release the weights soon after launch
➤ Pricing: $2/$10/$0.20 per 1M input/output/cache hit tokens, 33% lower than Kimi K3 on all three rates.

Congratulations to the team at @Kimi_Moonshot on the release!

Ken_dev

@CodeToTheMoons

Oct 15

@levelsio

@levelsio

Oct 14

Generational level of cope going on right now by software engineers that AI has not affected them, is not affecting them, and will not ever affect them

Ken_dev

@CodeToTheMoons

Oct 14

Ken_dev

@CodeToTheMoons

Oct 13

has anyone tried pointing a harness to an SNES ROM for a reverse engineer / Rust rewrite? Mega Man X written in Rust currently floating around in my dreams...

Ken_dev

@CodeToTheMoons

Sep 30

Google DeepMind

@GoogleDeepMind

Sep 30

Introducing Gemini 4 Argon – our new frontier model.

It’s built for complex workflows across coding, enterprise knowledge work, and cybersecurity defense – rolling out today to a set of trusted testers through our Fairwind Program.

Ken_dev

@CodeToTheMoons

Aug 20

Bun

@bunjavascript

Aug 20

Bun 1.4

- Fixes over 2,900 GitHub issues
- +1,517 tests from the Node.js test suite
- Reduces idle CPU by 5x
- Reduces memory usage by up to 35%
- Starts up to 50% faster on Linux
- Rewrites Bun in Rust

Thank you everyone who contributed since Bun 1.3!

bun.com/blog/bun-v1.4