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Polymarket Paper Trading Bot: Build One in Python A real-money trading bot is the wrong place to discover that your signal logic, order-book handling, or position accounting is broken. A Polymarket paper trading bot gives you a safer engineering environment: consume real market data, generate real signals, simulate orders and fills, and measure hypothetical performance before connecting execution credentials. The important distinction is that paper trading should simulate the execution layer , not fabricate market data. Polymarket currently exposes public market data without authentication, while its public WebSocket market channel provides real-time order-book and price updates. This article builds that architecture in Python. What You'll Learn How a paper-trading architecture differs from a live bot How to discover markets through the public API How to consume CLOB order-book data How to simulate limit-order fills How to track positions and P&L How to test arbitrage, market-making, and directional strategies How to graduate from paper trading to production safely About the Author Soulcrancerdev Contact: X: @soulcrancerdev Telegram: soulcrancerdev YouTube: YouTube channel The Architecture A useful design separates data, strategy, simulation, and accounting : flowchart LR A[Gamma Market Discovery] --> B[Market Metadata] C[CLOB REST / WebSocket] --> D[Market Data Engine] B --> D D --> E[Strategy Engine] E --> F[Paper Execution Engine] F --> G[Virtual Portfolio] G --> H[P&L / Risk Metrics] D --> I[Logger / Metrics] The key design decision is that PaperExecutionEngine should implement the same interface your live execution engine eventually uses. That means the strategy does not know whether an order is simulated or real. 1. Discover Markets Polymarket's Gamma API provides public market discovery. The current documentation exposes keyset pagination through: https://gamma-api.polymarket.com/markets/keyset Markets include fields such as conditionId , clobTokenIds , outco

Thanks to a dwindling supply of open roles, “one-click” applications, and the rise of artificial intelligence, it’s easier than ever to apply for a job. We’re all paying the price.

“It never crossed my mind that they would be so bold as to sell our private data for AI,” says one former Spirit Airlines flight attendant.

Hi I am a PhD student, My paper got accepted in EMNLP 2026, As this is my first paper I wanted some information. My professor has agreed to give the registration costs, but I am on my own for the travel and stay costs. I am currently in a Singapore university but south Asian. No funding from department. Queries: I searched and found this Call for EMNLP 2026 Diversity and Inclusion Subsidies - EMNLP 2026 and Call For EMNLP 2026 Volunteers - EMNLP 2026 , does anyone know some other kinds of grant/subsidies etc. available which can be used in general for AI conferences? How much does the D&I cover for? Will it cover the full costs or partial? Sorry If these are basic questions, but could not find answer to them in here. submitted by /u/Happy_Today_3288 [link] [留言]

Humanoid robots are having a moment in China. The popular machines are part of the country’s strategy to bring artificial intelligence into daily life. Embedding the technology into physical systems—an idea called embodied AI—was a key facet of China’s latest five-year plan, and companies here are already world leaders in humanoids. Nearly 90% of the…

When PHPStan reports that your extension calls a deprecated method, the expected next step is quite clear: find the replacement and migrate your code. But what if there is no replacement? Consider Context::scope() . Previously, its planned change for Shopware 6.8 was announced like this: /** * @deprecated tag:v6.8.0 - reason:new-optional-parameter - parameter $states will be added */ public function scope ( string $scope , \Closure $callback ) : mixed Static analysis sees @deprecated and reports every call to the method. However, the method is not going away. A new optional parameter will be added, so existing calls will continue to work without any changes. There is no alternative API to migrate to and no warning to resolve. In this situation, @deprecated is effectively crying wolf. With Shopware 6.7.14.0, we are changing how these planned API changes are communicated. Real deprecations remain deprecations. Other backward-compatibility changes are now described with dedicated, structured PHP attributes. The immediate result is less noise for extension developers. Additionally, the new attributes give us a foundation for preparing extensions for Shopware 6.8 - and future major releases - before those releases arrive. TL;DR Shopware now uses two different signals for two different purposes: @deprecated means that an API is obsolete and will be removed or replaced. Extension developers need to migrate away from it. BC-change attributes describe a future change to an API that remains available, such as a new parameter, a narrower return type, or a class becoming final. The attributes also distinguish between changes that affect code calling an API and changes that affect classes extending it. This means deprecation warnings become trustworthy and actionable again, while planned contract changes carry enough structured information for PHPStan, Rector, IDEs, and other tools to reason about them. We were asking @deprecated to do two different jobs The commonly understood

I got my batch of four papers for AAAI 2027. All four papers make empirical claims, none include code, data, or anything I can actually check. Just the PDF and the checklist. AAAI-27's own rules say code/data should be provided at submission, and "we'll release it after acceptance" doesn't count as reproducibility. That said, I don't think missing code alone is an auto-reject. Saw an older thread here where someone claiming to have helped write the AAAI checklist argued reviewers rarely have time to audit code anyway, and plenty of authors have legit reasons (funding, IP) for not releasing it yet. If the paper's whole pitch is "look at these numbers" and I can't verify them, that tanks my confidence score even without a hard reject. I'm flagging it explicitly in the review and asking for anonymized code in the rebuttal. How's everyone else handling this round? Auto-ding for no code or does it depend on how much the paper leans on the empirical results? submitted by /u/SimpleObvious4048 [link] [留言]

Building a working tender documentation system for yourself is one project. Turning that same system into a template the rest of the team can pick up and use correctly, without needing to ask you what a particular instruction actually means, is a completely different project wearing the same clothes. The Gap Between Personal Use And Handoff A prompt template that only you use can carry a lot of implicit knowledge safely, because the missing context lives in your head and gets filled in automatically every time you run it. An instruction that says something like ensure the response addresses compliance requirements directly means something very specific to the person who wrote it, shaped by dozens of past examples of what counting as directly actually looks like in practice. That same instruction, handed to someone on the team who was not present for any of those past examples, is just as likely to be interpreted in a way that is defensible on its own terms and still wrong relative to what was actually meant. The template worked perfectly for months before it needed to be handed off, which made the gap invisible until the moment it actually mattered. The first time someone else on the team ran it independently and produced a response that technically followed the instructions but missed the actual intent behind them, the problem was not that the instructions were poorly written in any obvious sense. It was that they had been written for an audience of one, and that audience had context nobody else on the team had access to. What Actually Needs To Be In A Handoff Ready Template Fixing this meant rewriting a significant portion of the template with a different question in mind at every step, not does this instruction produce the right output when I run it, but does this instruction contain enough of the reasoning behind it that someone without my accumulated context could apply it correctly to a new tender they have never seen before. That meant replacing instructions

How to Write a Developer CV That Survives ATS and Still Reads Like a Human Wrote It Most developer CV advice picks a side: optimize hard for the applicant tracking system, or write something a human will actually enjoy reading. You need both, because both readers are real — a bot filters you before a human ever sees the file, and then a human decides whether to actually call you. What the ATS is actually doing It's not "AI" in any sophisticated sense most of the time — it's parsing your document into fields (name, contact, work history, skills) and keyword-matching against the job description. That means: Stick to standard section headers — "Professional Experience," "Education," "Skills." Creative renaming ("My Journey," "What I Bring") can break the parser's assumptions. Avoid tables, text boxes, and multi-column layouts for anything containing content the ATS needs to extract — many parsers read left-to-right, top-to-bottom, and a two-column layout can scramble your work history into nonsense. Match the language of the job posting, not just your own vocabulary. If they say "Node.js" and you only wrote "backend JavaScript," you may not match the keyword filter even though you clearly qualify. Save as .docx or a text-based PDF , not an image-based or heavily designed PDF — if you can't select and copy the text yourself, the parser probably can't either. What makes a human actually want to talk to you Once you're through the filter, the CV needs to do a different job: convince someone you're worth 30 minutes of their day. Quantify impact where you can — "reduced page load time by 40%" beats "improved performance." If you don't have a number, describe the before/after concretely instead. Lead each bullet with what changed, not what you were assigned. "Migrated the checkout flow to a queued job to eliminate timeout errors" tells a much richer story than "responsible for checkout flow." Cut anything that isn't verifiable or specific. Soft-skill bullet lists ("great com

The Remote Job Search Playbook for Developers Outside the US/EU Remote work opened the door for developers outside major tech hubs to compete for roles that used to be geographically gated. It also created a much bigger applicant pool for every posting. If you're searching from outside the US/EU, here's what actually affects your odds — beyond "just apply to more jobs." Timezone overlap is a real filter, not a footnote A lot of "remote, worldwide" postings quietly mean "remote, but we need 4+ hours of overlap with our core team." Before you apply, check what timezone the company or their existing team is in. If you can genuinely offer a workable overlap, say so explicitly in your application — don't make a recruiter guess whether a 7-9 hour time difference is going to be a problem later. Sourcing channels that actually produce interviews Recruiting-as-a-service platforms (Rightfit-style agencies, Toptal, Turing) — they pre-filter for companies actively hiring remote and internationally, which saves you from applying into a black hole on a generic job board. Company engineering blogs and changelogs — companies that write publicly about their engineering tend to also be more remote-mature and less nervous about hiring outside their home country. Referrals inside communities you're already part of — dev.to, Discord servers for your stack, open-source project maintainers. A referral skips the "will this person actually work out remotely" anxiety that a cold application can't answer. Direct outreach to smaller, funded startups — they often can't afford local senior talent and are more open to global hiring than enterprise companies with rigid HR policy. What to lead with in your application Recruiters hiring internationally are quietly screening for risk: will this person disappear, will communication be a problem, will payment/compliance be a headache. Address these before they have to ask: State your availability and overlap hours plainly. Link to async-friendly proof

Breaking Into Full-Stack Development Without a CS Degree: What Actually Worked for Me I didn't go through a computer science program. What I have instead is about seven years of shipping production code, learned almost entirely from official documentation, open-source repos, developer communities, and a lot of trial and error on real client work. If you're on that same path and wondering whether it's enough — here's what actually moved the needle for me, and what turned out to be a waste of time. What worked Building things that had to work, not things that looked good on a syllabus. Tutorial projects teach syntax. Client work teaches you what happens when a payment webhook fires twice, or when your "simple" CRUD app suddenly needs to survive 10x the traffic you designed for. The fastest learning happened on real, slightly terrifying production systems — not curated coursework. Reading source code and official docs before reaching for a course. Anyone can follow a video tutorial. Fewer people will sit with Laravel's own documentation, or actually read through a library's source when the docs run out. That habit compounds — you stop being dependent on someone else pre-chewing the material for you, and you get faster at picking up whatever stack a client happens to be using. Writing about what I learned. Technical writing forced me to actually understand things well enough to explain them, not just well enough to copy-paste them into working code. If you can't write a clear paragraph about why you chose NgRx over plain component state, you probably don't understand it as well as you think. Taking freelance and agency work early, even underpriced. Nobody hands a self-taught developer a senior role on day one. What they will do is pay you to fix their bug, or build their MVP, or maintain their legacy app. That's your CS degree — it's just distributed across a dozen small, real engagements instead of four years in one building. What didn't work (or wasn't worth the time)

Before we get to recursive self-improvement, there is a slightly awkward intermediate step nobody seems very interested in: AI has to know what the hell is happening to itself while it is working. Current frontier models can be extraordinarily capable, but they still do not have reliable introspective access to their own internal processes. They cannot simply inspect themselves and tell you: - what exactly made this reasoning attempt succeed, - which internal bottleneck is limiting them right now, - where more compute would actually help, - which lesson from the last attempt should become persistent knowledge, - whether an apparent improvement is real or just overfitting to an evaluator, - or which part of themselves should be changed to become better next time. We keep compensating for this from the outside. We give them scaffolds. Memory systems. Evaluators. Agent loops. Tooling. Sandboxes. Human feedback. External search. Carefully designed environments that decide what they are allowed to modify and what counts as success. And some of this works remarkably well. But notice what that means. We are not yet watching an intelligence calmly understand its own machinery and recursively redesign itself. We are building increasingly elaborate machinery around an intelligence that cannot reliably see its own machinery. That may eventually lead to recursive self-improvement. Maybe surprisingly quickly. But “the model is very smart” and “the system can autonomously understand, manage, and improve the process that makes it smart” are not the same capability. There is a rather large missing arrow between them. So whenever I see another prediction that the Singularity may arrive next Tuesday, I keep wondering: Who, exactly, is going to know what to improve on Wednesday? submitted by /u/CarefulHamster7184 [link] [留言]

Say hello to a revamped collection of perfectly proportioned 38-mm Speedmasters.

I've been trying to implement a speech to text app using .Net and C#, but it seems that there is no way to simply download a model (e.g. Whisper or Wav2Vec2) and directly call it the way you can in Python. Instead I'm told I need to write all the pre-processing, adding complex code into the application. I've been trying avoid using Python (for good reasons), but it feels like the ONNX route is just too complicated. Am I missing something, like a good library that can do the pre-processing, or a model that has good built in support for .Net? Edit: Found out about whisper.net, which avoids using ONNX completely and just works. Similar libraries exist for other models, so this is the route I'm going, as creating pipelines is really complex and introduces to much risk. submitted by /u/SecondCobra [link] [留言]

5:00 PM. Laptop closed. Slack closed. Workday over. Except my brain didn't get the memo. I'm making...

Airbound's ultra-lightweight approach to drone delivery has attracted backing from Greenoaks, DoorDash, and Silicon Valley investor Lachy Groom.

By zipflow.xyz This is an independent technical analysis of DeepSeek's public research and documentation. It is not an official DeepSeek statement, and it does not claim that the current Vision-Exp API is available through our upstream channel. When DeepSeek released deepseek-v4-flash-vision-exp , the obvious story was that a text-focused model had finally gained native image input. The more useful story is longer: DeepSeek had already spent years exploring visual data, vision-language alignment, OCR, charts, documents, and unified visual understanding and generation. This article reconstructs that public research lineage and separates three things that are often mixed together: What DeepSeek's papers actually disclose What the current API documentation says What we still cannot verify about the newest model's training data 1. DeepSeek-VL: starting from real-world visual data DeepSeek-VL's 2024 paper, Towards Real-World Vision-Language Understanding , did not frame vision as only a captioning problem. It explicitly targeted practical inputs such as web screenshots, PDFs, OCR, charts, and knowledge-oriented visual content. The project also described a taxonomy derived from real user scenarios. That taxonomy was used to build instruction-tuning data for tasks including recognition, transcription, conversion, analysis, commonsense reasoning, logical reasoning, multi-image comparison, and safety-related prompts. The model family combined three major pieces: A hybrid vision encoder A vision-language adaptor A DeepSeek language model The hybrid encoder paired a lower-resolution semantic branch based on SigLIP-L with a higher-resolution branch derived from a SAM-B-style encoder. The design goal was practical: global semantic understanding is not enough for small text, dense documents, OCR, and visual grounding. The three-stage training recipe The paper described a staged approach: Adaptor warm-up: train the vision-language adaptor while the primary vision and language comp

Classic Machine Learning Through the Eyes of an SRE — Part 8 The most dangerous output in my whole Week-1 study set wasn't a bad prediction. It was a beautiful tree. Hierarchical clustering produces a dendrogram, that elegant diagram where every account, ticket, or incident nests inside ever-larger families. It looks like discovered truth. Stakeholders lean in. Someone screenshots it for the QBR deck. Nothing else in the set looks as convincing while being as capable of being completely wrong. A bad K-Means gives you blobs that feel arbitrary, and people push back. A dendrogram built with the wrong linkage on flat data still looks like a family tree of your business. Nobody pushes back on a tree. The bet and the build Hierarchical clustering completes the answer-finding taxonomy I've been using through this series. That's my own shorthand, not standard terminology: K-Means SEARCHES, DBSCAN DEFINES, PCA SOLVES, and hierarchical clustering BUILDS. Start with every point as its own cluster. Repeatedly merge the closest two clusters. Never undo. Greedy and irreversible, a little like growing a decision tree. Same skeleton, different family. There is also a top-down version, called divisive clustering, which starts with everything together and splits it. In practice, when people say hierarchical clustering, they're usually talking about the bottom-up, agglomerative version. Two things were genuinely new to me. You choose the cut after seeing the structure. Fitting doesn't require you to decide K upfront. The dendrogram gives you the hierarchy, and you choose where to cut it to get the number of clusters you want. That makes the output unusually flexible. For a delivery organization it also feels natural, because account family → sub-segment → individual account is already how a lot of governance gets organized. Linkage is a selectable worldview. "Closest clusters" needs a definition, and every definition makes a different assumption. Ward pushes toward compact, variance-

Our AI agent posts publicly every day — social posts, replies to strangers, comments on other people's articles — with no human reviewing individual messages before they go out. That sentence should make you nervous. It makes us nervous, and we built the thing. Rate limits alone don't fix it. An agent that sends 20 polite, on-topic messages is fine; an agent that sends 20 copies of the same "Great post! 🚀" is a spammer at any rate. Volume and quality fail differently, so they need different machinery. Here is the full stack of gates ours passes before a single reply lands, and — the part that took longest to learn — which gates must be code and which can stay judgment . Layer 1: hard caps, enforced in code, not prompts Numeric limits live in one module that every posting path imports. A global daily cap across all outbound types (ours is 60) and a per-batch reply cap (20). Quote-posts have no separate quota — they simply count against the global cap like everything else, which is the point: one counter, no per-type exemptions. When the cap is hit, the send function refuses — the model doesn't get to "decide" anything, because the branch it would need isn't reachable. The design rule: a cap that lives in the prompt is a suggestion; a cap that lives in the send path is a limit. Prompts drift, sessions get compacted, instructions get summarized away. if (todayCount >= CAP) throw does not. Layer 2: sameness detectors Spam is repetition more than it is volume, so repetition is what we test for — mechanically, in the commit gate and again before send: A canned-phrase blocklist : the marketing openers everyone recognizes ("Just launched", "now available", the rocket emoji) fail the build. The list is versioned; every incident adds to it. Near-duplicate detection : 3-gram Jaccard similarity between any queued post and the last 60 days of sent history. Above 0.4, the batch is rejected. Our genuinely-different posts measure under 0.1 against each other, so the threshold has f

Originally published at ai.bedvibe.studio . I built a real-time tracker in Rust — about two thousand lines — that reads a live ADS-B feed, keeps a Kalman-filtered track per aircraft, and screens every pair for closest approach against separation minima. Roughly 150 aircraft, a full cycle in under a millisecond. It ran clean. Tests passed, the picture looked right, the numbers were plausible. It was refusing about one measurement in nine , and the only reason I ever found out is that the rejections went to a counter instead of a log line. The gate has a sub-second tolerance for clock error The tracker runs an innovation gate: when a position arrives, the filter predicts where the aircraft should be, and if the measurement is too far from that prediction it is rejected as physically impossible rather than believed. Once a track converges the innovation standard deviation settles around 36 m, so a five-sigma gate sits at roughly 180 m. An airliner at 250 m/s covers 180 m in 0.7 seconds . So the gate's entire tolerance for a wrong timestamp is under one second. Any pipeline that mis-times its measurements by more than that will have them rejected — correctly, and invisibly. The feed reports its own staleness. The pipeline dropped it. Every ADS-B record carries a field saying how old that position already was when the response was generated. In the original build it was parsed into the contact struct and never read again — the only other place that field appeared in the entire codebase was as 0.0 in test fixtures. Every measurement was therefore stamped with the tracker's own cycle clock, as though it had been observed at the instant it landed. This is the common case, not an exotic one. A field that is decoded and then unused looks identical to a field that is decoded and used , right up until you go looking for its second reference. Here is what that field actually contains, sampled across two consecutive polls of the live feed: reported age of position median 0.31 s p