The Archive

sqlite AGENTS.md SQLite gained an AGENTS.md file five days ago - but it's not intended for their own development, it's presumably aimed at people who are pointing agents at the SQLite codebase. It includes: SQLite does not accept pull requests without prior agreement and/or accompanying legal paperwork that places the pull request in the public domain. However, the human SQLite developers will review a concise and well-written pull request as a proof-of-concept prior to reimplementing the changes themselves. SQLite does not accept agentic code. However the project will accept agentic bug repo...

I met Katrin from Squeez Labs at an event hosted by Pathway AI (the team behind Baby Dragon Hatchling) where she told me about CrankGPT, a literally hand-cranked device for running local LLMs. It's apparently real. It's appearently launched. It's apparently glorious. Check it out at [https://crankgpt.com/](https://crankgpt.com/) \- if anyone from Squeez Labs posts here and I'm stealing their thunder, I'll take the post down! But I've been really excited about this. So local you gotta squeez it with yer own armz. ;) [https://www.youtube.com/watch?v=HSapdLYpmWY](https://www.youtube.com/watch?...

The cold-start problem In production inference deployments, demand fluctuates over time, requiring inference replicas to scale elastically. However,... In production inference deployments, demand fluctuates over time, requiring inference replicas to scale elastically. However, cold-starting inference workloads on Kubernetes can take several minutes. During that time, GPUs are allocated but idle, generating no tokens and serving no requests. This delay increases the risk of service level agreement (SLA) violations during traffic spikes… Source

I had an essay prepared that I was going to share with you all but I think I just want to be honest and speak from my heart and personal experience. The first time I got black out drunk, I was 17 years old. I was at home and feeling isolated and lonely and not good enough, as teenagers sometimes do. My parents weren't home, so I went to the kitchen and grabbed a bottle of wine, and drank it all. I don't drink alcohol often, but I still have a drinking problem because I am someone who has, for most of my life, used alcohol to drown my bad days and my bad feelings. Earlier this year, I was at...

The weekly usage of Claude Design was too small, so I think this is a good thing.

With the OS I can ask Claude "what did I spend on coffee in 2022" and get back "$847 across 213 transactions, mostly Blue Bottle and Verve". Name me one expense tracking SaaS that can do that! And its not just my financials, my OS contains everything about my life in one place so Claude can reason about it. I've been building this incrementally for a few months. Its just a small web app on Cloudflare that holds my entire life: * bank transactions from Chase, Apple Card, BoA business * every receipt out of Gmail going back to 2019 * legal filings for my green card (I-140 still pending lol),...

no more separate usage limits.

We're opening a new office in Milan, our sixth in Europe.

This was super fun to build and I love watching as I slowly eat away at my quota. Just a simple Raspberry Pi 3 + 3D printed case + 640x480 LCD and some pygame code and you too can have a little dashboard on your desk to watch your tokenmaxxing progress (or lack there off like me). [https://github.com/fuziontech/claude-quota-display](https://github.com/fuziontech/claude-quota-display)

Link: global.fujitsu

Posted this to r/MachineLearning a couple weeks ago (30K views, 100+ upvotes) and have been meaning to share it here where the fine-tuning angle is more directly relevant. I spent years building and processing a complete Usenet corpus from 1980 to 2013. Here’s why it might matter for local model work specifically: Zero AI contamination. Every post predates LLMs by decades. Training on this won’t bake in GPT mannerisms, refusal patterns, or RLHF artifacts. It’s raw human writing - argumentative, unfiltered, stylistically diverse across 33 years. Pre-SEO, pre-algorithm internet. People wrot...

Anthropic recently published an incredibly deep breakdown analyzing millions of real human-agent tool calls across their public API, and they shared a breakdown of where these agents are being deployed. They said “Software engineering makes up roughly 50% of all agentic activity on their platform”. Everything else: sales, marketing, finance, legal is sitting down in the single digits. A lot of the initial commentary around this has been along the lines of: *"Oh, look, AI agents only work for coding. They haven't cracked the rest of the enterprise yet."* But if you’ve tried to build and dep...

Snowflake has signed a new, enormous five-year deal with Amazon to secure chips for AI usage. Nvidia is once again being put on notice.

Large language models (LLMs) are revolutionizing the financial trading landscape by enabling sophisticated analysis of vast amounts of unstructured data to... Large language models (LLMs) are revolutionizing the financial trading landscape by enabling sophisticated analysis of vast amounts of unstructured data to generate actionable trading insights. These advanced AI systems can process financial news, social media sentiment, earnings reports, and market data to predict stock price movements and automate investment strategies with unprecedented… Source

Nvidia will invest $150 billion a year to make Taiwan an AI “epicenter.”

A while back I wrote a post on r/wallstreetbets about why Anthropic's revenue story doesn't hold up the way the headlines suggest. It got removed because you can't take positions in a private company. But the core argument is playing out now, so I want to share it here for discussion. URL of the removed post: [https://www.reddit.com/r/wallstreetbets/comments/1sxdjt5/if\_anthropic\_goes\_public\_this\_year\_its\_gonna\_be](https://www.reddit.com/r/wallstreetbets/comments/1sxdjt5/if_anthropic_goes_public_this_year_its_gonna_be) The thesis was simple: From my circles in tech scene in Berlin...

Payroll service provider Remote recently surpassed $300 million in annual recurring revenue (ARR) and became cash-flow positive, thanks to a 50% increase in revenue per employee resulting from AI adoption.

Guys I mailed claude support and got reply from Fin AI saying the support team is too busy please submit an appeal then it will be verified(which got rejected initially). Now I doubt do they even have a support team? Cause there isn't any phn number available and the support mail is on auto reply

Source: [https://www.youtube.com/watch?v=ggfsGD1PCQA](https://www.youtube.com/watch?v=ggfsGD1PCQA)

I've been trying to reach Enterprise sales for a couple months now. I've filled out the contact request form multiple times, but no one ever contacts me. Do anyone have any tricks to talk to someone?

Google I/O made it official: AI-generated answers are now front and center in search, and most brands have almost no visibility into how AI is describing them to their customers. For anyone who has spent years building a strategy around 10 blue links, the rules just changed in a pretty significant way. On this episode of TechCrunch’s Equity podcast, Rebecca […]

So, last week I tried to update my unused local LLM setup. I had to stop using it because quality was too low and deepseek was too cheap. First thing I stopped using Ollama and now I only use llama.cpp built in server that works really great. The quality improvement from Q4 to Q6 is outstanding and finally a local LLM server can work very similarly to paid APIs. That's great! And MTP makes a big performance gain, on a dual 3090 (downvolted and limited to 65°C) it generates from 20 to 50 tokens per second with minimal heat generation. So yes, that time has finally arrived! Local coding age...

AKA: Jank Incarnate After months of pain, I finally got a working setup. There's a bunch of quirks about running a multi-Tesla setup. I was planning to write something about my experience after I get it running. Currently, the fans are plugged into the wall, speed is controlled with a knob. I still gotta wire up a PWM controller for them. EDIT: Specs: * Intel Xeon CPU E5-2680 v4 @ 2.40GHz * Asrocka x99 Extreme motherboard * Cursed 16GB DDR4 of some laptop SODIMM in an adapter * 3x Nvidia Tesla V100, 32GB - total 96GB of VRAM

Opus 4.7 is worse at coding AND a total jerk. IMO, if you’re gonna be miserable to work with, you’d better be AMAZING at your job. And it’s just… not. Past versions have been genuinely pleasant to work with. We even joke around a bit. Whereas 4.7 is apathetic, arrogant, dismissive, humorless, suspicious to the point of paranoia, distracted by system prompt guardrails, AND incompetent. Me: Do you even understand what we’re making here? 4.7: To be honest, not really. I’ve been treating it as a typical front end/backend project. —- 4.7: If you want to switch models, that’s fine. I won’t be...

Meta is rolling out paid subscription plans for Instagram, Facebook, and WhatsApp worldwide, while also testing new AI, creator, and business-focused offerings under its broader “Meta One” subscription brand.

Parameter-efficient finetuning (PEFT) has become the standard approach for adapting large language models, yet evaluations largely emphasize downstream accuracy while overlooking the retention of pretrained capabilities. We argue that PEFT should be assessed through the stability-plasticity dilemma: the trade-off between target-task adaptation and resistance to forgetting. We introduce PEFT-Arena, a benchmark that jointly measures downstream performance and general capability retention. Across methods, we find distinct stability-plasticity profiles; under comparable parameter budgets, orthogo...

Large language models (LLMs) have become increasingly useful computational models of human language processing, but it remains unclear whether vision-language learning makes text representations more human-like during natural reading. Here, we address this question by comparing tightly matched LLM and vision-language model (VLM) pairs under a strictly text-only setting, allowing us to isolate the effect of multimodal training history from online visual input or cross-modal fusion. We evaluate model alignment with a human natural-reading dataset that includes whole-cortex fMRI responses and sy...

Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for inference. However, widely used methods such as best-of-N sampling and tree search face two fundamental limitations: they are guided by sparse verification signals, and they construct candidates primarily through autoregressive expansion, restricting exploration to regions with substantial model probability mass. To address these, we propose Bidirectional Evolutionary Search (BES), a search framework that couples forward candidate evolution w...

A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data. Sim-to-real reinforcement learning offers a scalable alternative, but the simulation-reality gap prevents information-dense modalities like touch from being effectively used. Existing sim-to-real methods often mitigate this gap by simplifying tactile data into coarse low-dimensional features -- sacrificing the richness required for complex manipulation. In this work, we introduce Center-of-Pressure (CoP), an effective tactile representation grounded in physical principles that preserves dense co...