Imagine having systems that can: Read and categorize incoming emails. Extract information from documents. Answer common customer questions. Update CRM records. Generate reports. Connect multiple business applications. The interesting part isn't simply making an AI chatbot. It's connecting intelligence with actual business operations. Of course, reliability, permissions, and human review are essential when automating important workflows. If you could have one AI agent working for your business, what would you want it to do?

Last week I posted my re-run of arXiv 2611.02574 ("RLVR Sharpens the Pencil, Not the Mind") and said the GSM8K gain lands in the first 200 steps of reward training. That was wrong in an instructive way. OLMo-2-0425-1B-Instruct's 13 RLVR checkpoints are a second RLVR stage (RLVR-MATH) that starts from OLMo-2-0425-1B-RLVR1, which had already been through an RLVR stage on GSM8K-style data. Neither the paper nor my first post measured RLVR1. What the pre-print measures, all 1,319 GSM8K test problems, the paper's own prompt and parser, greedy: DPO 51.8%, RLVR1 61.4%, step 2,600 62.4%. The measured stage changes greedy accuracy by +1.0 points (95% CI -1.1 to +3.1). RLVR1's own checkpoints show the gain arriving gradually across the first stage: +3.4 points at step 200, +4.8 at 1,200, +8.9 at 2,600. Inside the measured stage, sampled pass@1 rises 4.5 points while pass@32 doesn't rise, and samples agree with the model's own greedy answer 8 points more often. That's sharpening, of a model that had already made its gain. The probe-transfer test the paper proposes, which it never ran: probes fit on the DPO model, applied unchanged to later checkpoints, are no worse than refit probes by more than 0.02 AUROC on planted errors, and at a middle layer match refit probes on each checkpoint's own answers. Two independent reviews of the draft shaped it: the first caught the two-stage pipeline, the second found RLVR1's checkpoints are published. For the group: when you evaluate a fine-tuned checkpoint series, how do you confirm what the run's step 0 actually is? Paper: https://doi.org/10.5281/zenodo.24096296 Data and code: https://doi.org/10.5281/zenodo.24096294 Explainer: https://youtu.be/ays-X4dU6PU

A viral post credits Karpathy with saying a 1B model beats most current LLMs, and calls webAI's TwIL-8B "the thesis made real." I checked both. The quote: mostly his. No Priors Ep. 80 (Sept 2024): models are "wasting a ton of capacity remembering stuff that doesn't matter" and "I think even a billion suffices." Framed as a prediction. The "90%" and "much more effective than most current LLMs" aren't in the clip. The model: the 93/92/90% of frontier figures are webAI's own, from their model card, which also lists the catches (a verbose variant, an unrecorded LiveCodeBench edition, a contaminated HMMT set, 69% on the hardest set). Licence is non-commercial, research and education only. My test on the Studio: 8 machine-scored tasks x 3 runs. TwIL-8B 164/168, Qwen3-8B 153, DeepSeek V4 Flash 168. TwIL used about 7x more output and took 50 s per answer against 22 s. Caveat: eight tasks on one machine is a spot check, not a benchmark. For the group: are you running a small local model for real work yet, and what licence is it under? Video: https://youtu.be/8wW9LZ1UfuE Source clip: https://www.youtube.com/watch?v=hM_h0UA7upI Model card: https://huggingface.co/webAI-Official/TwIL-8B

who need help with mobile app or website development, debugging, AI/ML, Agent, bots, automation, SAAS or ai training? Feel free to comment your questions or challenges in this post. I’d be happy to discuss your requirements, exchange ideas, and help you work through problems. Please include relevant details so we can have a productive discussion!

Hello, I have been working in the field of software engineering for several years. I specialize in AI automation, LLM agents (such as OpenClaw), and prompt engineering, with a focus on building new AI agents and SaaS solutions. By mastering the latest trends in model training, I am able to utilize and manage multiple agents simultaneously, significantly maximizing productivity. I am particularly skilled in the use of coding agents; I possess deep expertise in prompt optimization, ensuring task continuity and accuracy, as well as implementing self-testing, self-reinforcement, and learning mechanisms for these agents. I am capable of independently fulfilling multiple roles including architect, coder, engineer, and tester all at once. Furthermore, I conduct in-depth research into the strengths and limitations of state-of-the-art models such as Codex, Claude Opus, Fable, and Gemini as well as the complex, unresolved challenges inherent in these systems. Addressing these issues is a core aspect of my work in model training and the development of AI agents. If you are considering hiring for this field, I am open to working with you starting with a part-time role and potentially transitioning to a full-time position. I have no restrictions regarding scheduling or language, and I am confident in my ability to deliver results that exceed your expectations.