The Archive

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...

Functional music applications, from consumer focus and sleep aids to clinical interventions, share a distinctive recommendation problem: success is defined by the listener's affective state, but online experimentation on emotion is ethically constrained, particularly for clinical populations who cannot reliably skip a song or report distress. We describe AMRS, the Affective Music Recommendation System deployed on LUCID's health-and-wellness platforms, which serve clinical users (primarily older adults with neurocognitive conditions) and consumer-wellness users across energize, focus, calm, an...

Class-Incremental Learning (CIL) is important in building real-world learning systems. In CLIP-based CIL, the model performs classification by comparing similarity between visual and textual embeddings obtained from template prompts, e.g., ``a photo of a [CLASS]''. This seemingly monolithic matching process can be decomposed into two conceptually distinct stages: attribute extraction and attribute aggregation. For example, a model may recognize cat using attributes such as fur texture and whiskers. When learning a new class like car, the model must extract additional attributes like wheels an...

Agentic AI systems capable of autonomous planning and extended environmental interaction pose a fundamental control problem: how can humans maintain meaningful oversight of systems that may exceed their own capabilities? Existing approaches to scalable oversight rely on complex assumptions, remain largely heuristic, or lack practical methods for sequential settings with statistical guarantees. We introduce Calibrated Collective Oversight (CCO), which aggregates diverse auxiliary scoring functions into a penalty measuring deviation from a conservative baseline. Inspired by Attainable Utility P...

Long-term memory is increasingly important for personalized AI agents, yet existing benchmarks and methods remain largely text-centric. Even when images are included, the user-specific information needed for later questions is typically recoverable from text alone, and most memory systems reduce image turns to generic captions. Yet images often carry personal information that text rarely states -- both explicit evidence, such as recurring user-associated entities, and implicit evidence, such as latent user facts inferred from visual or multimodal cues. We introduce a benchmark for personal vi...

Visual outcomes are increasingly central to multimodal large language models, making reliable and fine-grained verification essential for scaling generalist foundation models. In this work, we investigate multimodal meta-verification, which leverages verifier-generated rationales rather than decision-only signals, and explore how to effectively incorporate meta-verification feedback into multimodal verifier training. We identify two key findings. First, symbolic verifier outputs (e.g., bounding boxes) outperform textual explanations as meta-verification rationales, enabling efficient rule-bas...

Vision-Language-Action (VLA) models unify perception, reasoning, and control within a single policy, yet their multi-billion-parameter backbones and diffusion-based action heads make on-device deployment prohibitively expensive. Prior quantization efforts offer only partial solutions, compressing the LLM backbone while leaving the DiT action head at full precision, or resorting to mixed-precision schemes, driven by the belief that uniformly quantizing the action head is inherently unstable. We challenge this assumption with Omega-QVLA, the first training-free post-training quantization framew...

Free-text explanations extend human label variation (HLV) beyond label disagreement by revealing the reasoning and preferences behind annotators' decisions. We study whether large language models (LLMs) can learn and reproduce such annotator-specific label-explanation behavior. Using two sentence-pair tasks with four annotators each -- natural language inference and paraphrase judgment -- we first analyze whether annotators exhibit stable individual patterns. We find that such patterns are weak at the single-annotation level due to strong input-content effects, but become detectable after inp...

Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to multiple hours, posing a major hurdle for existing deep learning methods due to two major factors: (1) existing EEG models are predominantly built upon the attention mechanism, incurring quadratic scaling as the sequence length increases, and (2) raw EEG signals must be processed in a sliding-window fashion due to fixed-length input requirements, preventing global understanding of the entire signal. To this extent, we propose CaMBRAIN - the firs...

On-policy self-distillation (SD) improves LLM reasoning by using teacher-side privileged information (PI) to turn sparse verifier outcomes into dense token-level supervision. Existing methods usually assume trusted PI, such as reference answers or successful traces. We ask whether PI can instead come from an experience-derived skill bank, where retrieved skills are compact and reusable but may also be irrelevant or misleading. We propose Skill-Conditioned Gated Self-Distillation (SGSD), which formulates skill-based SD as teacher hypothesis validation rather than unconditional imitation. SGSD ...

In the era of autonomous agents, machine-actionable data is critical for data-driven workflows. For more than a decade, semantic metadata like schema.org has anchored the FAIR principles (Findable, Accessible, Interoperable, and Reusable) for machine-actionable data and enabled discovery tools like Google Dataset Search. However, the rise of Large Language Models (LLMs) capable of navigating the unstructured web raises a fundamental question: Is semantic metadata still necessary for agentic data discovery, or can agents reliably retrieve actionable data directly from the web? We present a com...

Datasets vs. inductive bias, world models, and programmable biology

Discourse particles, such as \textit{well} and \textit{kind of}, are crucial components that enable LLMs to ``speak'' more like humans. They are used to convey emotions, intentions, and interpersonal meanings. However, existing studies have not yet built a comprehensive understanding of LLMs' capabilities in handling discourse particles. Moreover, the limited number of studies focuses primarily on high-resource languages such as English, with little attention paid to Southeast Asian languages. In this paper, we (1) propose \textsc{MalayPrag}, a benchmark designed to systematically evaluate an...

I know this sub loves absurd LLM projects, so sharing my contribution while we wait for the new Qwen 3.7 models to drop! I successfully got a tiny LLM running inside an RTOS, running inside a custom-built JavaScript emulator for the Freescale ColdFire MCF5307, which is a derivative of the legendary [Motorola 68K](https://en.wikipedia.org/wiki/Motorola_68000) that powered the original Mac and Sega Genesis. The RTOS was written back in 2008 with three classmates for our embedded systems university course. It was lost to time, with the hardware and original ROM long gone. A few months ago, I d...

Vision classifiers can exploit spurious correlations, achieving high in-distribution accuracy yet failing under distribution shift. Existing approaches to bias mitigation and analysis often depend on curated datasets, spurious-attribute or group labels, or retraining, which may be infeasible once a model is deployed or the relevant bias is unknown. We present a bias-label-free, post-hoc method for identifying spurious concepts in frozen vision models, relying only on standard class labels from a held-out audit dataset. For each target class, we collect patches from inputs predicted as that cl...

Vision-language models (VLMs) generate fluent causal explanations, but current evaluations cannot distinguish linguistic plausibility from faithful causal reasoning. We introduce a dual-probe methodology that isolates these properties. The Text-Only Probe measures linguistic quality. The Chain-Text Probe requires models to first generate explicit causal chains. The Abstraction Gap (AG) metric quantifies the normalized performance difference. Evaluating eight VLMs on CAGE (Causal Abstraction Gap Evaluation), a benchmark of 49,500 questions across 5,500 images spanning Pearl's causal hierarchy,...