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I am trying to train an AlphaZero model for Othello on a 6x6-board. Having been warned that too little exploration during data generation can lead to models being overconfident and trapped in some tight region of the search tree, I started with the value c_puct = 4.0, and then reduced this to 3.5 after a few generations. Also, I added fairly peaked Dirichlet noise (alpha = 0.15) to the prior predictions at the root of each tree search, with the proportion epsilon = 0.25. The temperature was initially set to 1.0, and then reduced to 0.8 after 20 generations. Now, the models do improve in the sense that later models consistently beat earlier ones, but there is no significant improvement against the two benchmarks I use: classical MCTS, and a greedy agent. Against the latter, the models have a deplorably low win rate of less than 10%. As can be seen from the curve for the value loss on the validation data, the models don't seem to learn to predict values (which is why I have been hesitant to reduce c_puct further), but the prediction loss seems to behave more or less as it should. https://preview.redd.it/gjby4omfp35h1.png?width=640&format=png&auto=webp&s=4d2ba4716ade6ec4ce9b7f16605a2e6bd74c6baf I decided to test if the prediction targets become strongly peaked early on. For this, I compute the normalized entropies of these predictions, meaning that I divide the entropy by the log of the number of legal moves at the given game state. The plot below shows the mean values of these normalized entropies for the data sets created by the different generations of agents. https://preview.redd.it/5yk216zjp35h1.png?width=640&format=png&auto=webp&s=538f59f5da3671a20c0ef2e1afc1ec96da237107 Finally, I tested how the policy predictions of a fixed set of random game states vary with the models. Here, I have set the second model as a benchmark, and I compute the average Kullback-Leibler divergence between the predictions by the benchmark model and those by later models. This is display

I was thinking, with all the lay offs in tech. Would it be possible to start a company and just sort of catch the talent getting laid off? Obviously you would need an initial investment from something like an investment firm or an angel investor. I was just thinking that their could be an opportunity for some rich people to eat AI's lunch if they started a tech company with the laid of talent from the AI bubble. But also idk, I have never been in the valley, so I don't really know how it works. submitted by /u/LAN_scape [link] [留言]

This LinkedIn post argues that NeurIPS 2026 used a proprietary AI-text detector to desk-reject papers for alleged AI-policy violations, without validating the detector on the actual target distribution. The author then fed recent papers by NeurIPS Position Paper Track Chairs into the same detector and Pangram assigned them high AI scores, including 69%, 45%, 36%, and 24% AI. submitted by /u/Asleep-Requirement13 [link] [留言]

Article URL: https://www.ycombinator.com/companies/skyvern/jobs/1qRTlVx-founding-developer-marketing-open-source-ai Comments URL: https://news.ycombinator.com/item?id=48386588 Points: 0 # Comments: 0

New proposals will see the EU further invest in European startups and companies as it looks to strengthen its homegrown tech footprint.

Run AI agents across your local files and apps on Windows Discussion | Link

Daily AI stories personalised from your Google apps Discussion | Link

Recently got an email from "Paratype" that one of my sites was using a font that was unlicensed and we needed to pay a fee. I inherited the site from someone else so had no idea about the font, which was hidden in a Bootstrap library, so the decision was made to remove it completely. I replied to Paratype stating this and they replied, "Well, you were using it in the past, so you should pay us anyway." Obviously I'm going to tell them to piss off, but I was wondering how common this sort of thing is? I doubt they have much of a claim legally, the wording of the email is pretty weak sounding to me. submitted by /u/TConner42 [link] [留言]

Hey everyone, I'm a researcher at Carnegie Mellon University conducting a research study on how developers are actually using spec-driven development (SDD) in practice — things like writing SPEC.md files, PRDs, or structured natural-language specs before working with AI coding agents like Claude Code, Cursor, Kiro, etc. There's a lot of community knowledge about how to do SDD well, but almost no academic research on it. I'm trying to change that. What the study involves: One 45-60 minute semi-structured interview via Zoom Questions about your SDD workflow, what's worked, what hasn't, and how it fits into your SDLC No tasks, no tests — just a conversation about your experience Who I'm looking for: Have at least one year of active experience as a contributor or maintainer of any open-source GitHub project Have used SDD tools/workflows in that project (spec files, structured prompting, plan-mode workflows, etc.) 18 or older, fluent in English What you get: Honestly, nothing monetarily. But your experience will directly shape a taxonomy of SDD workflows and practices that I'll publish openly. Happy to share findings with participants who want them. Ethics/privacy : The interview will only be audio-recorded with your consent. Your responses will be kept confidential and de-identified in any published findings. If you're interested, fill out this short screening survey (5 min): LINK Or DM me / comment below with questions. Also happy to hear if there are other communities I should be posting in. submitted by /u/lost_researcher1 [link] [留言]

Run multimodal AI locally with an encoder-free architecture Discussion | Link

Hackers appeared to take over victims’ accounts even after Meta said it fixed its AI-powered support chatbot, which granted hackers access to victims’ accounts.

Amazon's updated search bar will now show you AI-generated images of products as you describe them. For now, the in-app feature only surfaces AI images of clothing and home goods, allowing you to tap on the image that best matches what you're looking for and search for similar-looking items. In a blog post, Amazon positions […]

It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/b

What can I say. Anyone claiming that F# is good mostly for finance and data processing and C# for everything else, has probably never written a single line of practical F# code. In previous two parts of the article, I tried to demonstrate that with F# you can achieve the same goals as with C#, but with less verbose, repetitive, structural code. How it started. At some point, developers realized that global state with unrestricted data access causes many side effects, producing insecure, error-prone, and hard-to-maintain code as software grows larger. That is when the idea emerged to bring data and the code operating on it together into a single unit, restricting direct access to the unit’s internal state and making software more secure and predictable. This is how data encapsulation was born. Alongside encapsulation, abstraction was introduced — the process of hiding how behavior works. Encapsulation ( hiding data ) and abstraction ( hiding behavior ) remain two foundational pillars of Object-Oriented Programming. And that is how OOP has worked ever since — developers bring data and behavior together ( classes ) and define abstractions for them ( interfaces ). For example, for C# developers — including myself — this has become a daily routine. And we rarely question it, because OOP languages like C# leave us little choice but to structure code this way. But if you ask yourself whether this repetitive routine is always necessary, the answer is — no. You don’t need OOP concepts to build stateless, streamlined request–response, data-processing pipelines, because in such systems there is no long-lived state to hide and protect. You have a request, and almost immediately you have a response. After that, everything is gone. That is what I tried to demonstrate in the first two parts of this article by applying FP concepts. And even if you have a classical desktop application, you don’t always need to approach it in an OOP way. Functional programming handles side effects no

Amazon will use visual search and AI to show AI generated product images that match your search queries. The retailer says it will help guide users to products.

It's no secret that Marketing Cloud Next (MCN) — or "Growth", or "Agentforce Marketing", or whatever they happen to be calling it this month — is the future of Salesforce marketing products. For the first iterations of it, MCN has seemed focused on attracting new customers with smaller email needs, as an on-core, Flow-based email product for people already in the Salesforce ecosystem. For existing users for Marketing Cloud Engagement (MCE... or traditionally, SFMC... or to the old schoolers like me who used it pre-Salesforce, ex-ExactTarget), and particularly for enterprise users, there have been some deal breakers with missing functionalities. Among those, a lack of AMPscript support has been #1. If you use AMPscript, you know it's infinitely more powerful than any drag-and-drop decision split block will ever be. It allows you to not just personalize an email with tokens/fields in your sending data, but to look up data from other tables, transform how it displays, and create data-driven dynamic versioning of your email. The idea of moving to a new MC that doesn't support AMPscript sounds like giving up one of the most powerful tools in your kit. But not for much longer. It's Here(-ish) In Salesforce's Summer 2026 Release Overview , AMPscript is called out as coming to MCN in PDF 1 , pages 353-355. And they acknowledge that this is a big deal for current MCE customers that want to continue using their historical code skills for great personalized emails. It does say the support will have a "targeted set of functions" which implies that not 100% of functions will be available immediately. AMPscript is a deep language and there are a lot of functions that probably needed dev work to migrate from MCE mechanics to MCN mechanics (for example, I'm sure data extensions are very different on the back end). I'd also guess that system strings might end up a little different from one system to another. So I wouldn't expect a 100% 1:1 recreation. Either way, this is a huge step

After shelving the original executive order last month, Donald Trump finally got on board Monday night.