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Reviving PapersWithCode (by Hugging Face) [P]

Hi, Niels here from the open-source team at Hugging Face. Like many others, I was a huge fan of paperswithcode. Sadly, that website is no longer maintained after its acquisition by Meta. Hence, I've been working on reviving it. I obviously use AI agents to parse papers at scale and automatically generate leaderboards (for now I'm the one verifying results). So far, I've only parsed high-impact papers for which I know they're SOTA, like Qwen 3.5 and 3.6, RF-DETR for object detection, DINOv3, SOTA embedding models from the MTEB leaderboard, the Open ASR Leaderboard for automatic speech recognition models, etc. For now, it includes the following: trending papers by default based on Github star velocity categorization by domain, e.g., OCR methods, which PwC used to have, e.g., RLVR eval results for high-impact papers, see e.g., Qwen 3.5 at the bottom leaderboards for each domain, e.g., MMTEB or COCO val 2017 support for citation counts (you can also see the most cited papers by domain!) automated linked Github, project page URLs, and artifacts (+ multiple repos are supported on a paper page) support for external papers beyond Arxiv, see e.g., DeepSeek v4 Harness reports for coding agent benchmarks, e.g., Terminal Bench "Sign in with HF" and Storage Buckets are used to store humbnails, paper PDFs, and overall data backups. I'm curious about your feedback + feature requests! Try it at paperswithcode.co https://preview.redd.it/whwji560fw1h1.png?width=3452&format=png&auto=webp&s=55bb7a30c1be58d140f7efcb07a31c6dac5693c7 See e.g. the SOTA leaderboard for Terminal Bench 2.0: https://preview.redd.it/98w9pi89fw1h1.png?width=3456&format=png&auto=webp&s=408fb64b0ba85ba24f55daa81d547d7c68e73951 A paper page looks like this: https://paperswithcode.co/paper/2602.15763 https://preview.redd.it/fiizit6dfw1h1.png?width=3450&format=png&auto=webp&s=9ea05a77ca5583a2fb395dccc95ba52c433362c5 submitted by /u/NielsRogge [link] [comments]

ChatGPT only lets you delete chats one at a time!! So I built a bulk delete dashboard!!

About a year ago I tried to clean up my ChatGPT chat list. I had something like 800 conversations, two years deep, mostly auto-titled "Untitled chat" garbage that I couldn't tell apart without opening. I sat down to delete the dead ones. Click chat. Click three-dot menu. Click Delete. Confirm. Click the next chat. Same thing. Repeat. After an hour I had deleted maybe 40 chats. Forty!! Out of 800!! That's the rate of clearing a 2-year history in something like three full workdays of just sitting there clicking confirm. I looked for a native bulk option. There isn't one inside ChatGPT itself. The closest is "Delete all chats" in Settings > Data Controls, which is the nuclear all-or-nothing button. There's no "delete the oldest 300" or "archive everything from before March". That's the entire native API. This seemed insane to me given how trivial "Select All plus Delete" is in literally every other product I've used since 2008! So I built the missing piece. **What I built** It's a Manage Chats modal inside a Chrome extension I ship called ChatGPT Toolbox (also runs on Edge, Brave, Opera, Arc). The modal lists every conversation in your account with checkboxes. Tick what you want gone, click Delete or Archive, and it runs through them in batches of 10 with a progress bar. [ChatGPT Toolbox Manage Chats Feature](https://preview.redd.it/097kho42ln1h1.png?width=892&format=png&auto=webp&s=3b9a9c517fa1005e968b9e664c08037b97795583) A few details that came out of dogfooding it: * **Color-coded age badges** on every chat. Green for the last week, blue for the last month, amber for the last 6 months, red for older than 6 months. The first thing I realized was that picking what to delete was the hard part, not the deletion itself, and age was the strongest signal for "I will never look at this again". * **Active vs Archived tabs.** Archive ended up getting more use than Delete in my own usage, because I was rarely 100% sure I wouldn't want a chat back. So I made archive a first-class action, not a second-tier option. * **Live progress bar** ("Deleting 23/50") on bulk operations. I tried it without and kept refreshing the page mid-operation thinking it was stuck. Adding the indicator stopped that completely. * **Search by title** to filter the list before you start ticking. Surprisingly useful even on the auto-generated nonsense titles because there's usually some keyword in there. * **Bulk export** to text, markdown, JSON, or PDF. Less critical for cleanup itself, but a few testers asked for it so they could save a chat outside ChatGPT before deleting it. I went from 800 chats to about 60 in 5 minutes using it. Most of those 5 minutes was deciding what to keep, not the deleting itself. **How does the workflow look?** Open the modal. List loads sorted by recency. Search to narrow it down if you want. Tick checkboxes. Hit Delete or Archive. Confirm. Progress bar runs through them. Done! If you've cleaned up a big ChatGPT history (with or without my tool, or with some clever workflow I haven't seen), would genuinely love to compare approaches in the comments.

20 Claude Skills for Marketing, Launch and Sales built for technical people

Curated this list of 20 Claude Skills for devs to get help with marketing, sales, launch: **Content** * human-tone: scans your copy against 18 GTM slop patterns and rewrites it. basically a linter for marketing language * cook-the-blog: researches a company, extracts SEO keywords, writes a case study in MDX, generates a cover image, pushes to GitHub. one command * noise-to-linkedin-carousel: paste rough notes or a voice transcript, get a carousel with hook and CTA. good for people who think faster than they write * tweet-thread-from-blog: turns any blog post into a 7-10 tweet thread. optionally posts to X via Composio * linkedin-post-generator: reads a GitHub PR or article, produces a post with the right hook and story arc **Sales** * discovery: run a proper needs assessment before you pitch anything. most DevRels skip this and go straight to the demo. biggest mistake. * objection-handling: "we already have something for this" and "our engineers will build it" are the two you'll hear constantly in developer sales. this is the one to internalize. * storytelling: case studies and narratives move technical buyers more than feature lists. if you can make someone see themselves in a story, the sale is mostly done. * qualifying-leads: not every inbound is worth chasing. knowing who to drop early saves more time than any outreach optimization. * closing: DevRels are usually great at building trust and terrible at asking for the next step. this one bridges that gap. **Intelligence** * gh-issue-to-demand-signal: give it a competitor's public GitHub repo. clusters open issues into demand categories, scores by engagement, outputs a GTM messaging brief. surprisingly useful for competitive research * where-your-customer-lives: give it your ICP, it searches Reddit/HN/DuckDuckGo to find the actual communities your customers are in. per-channel entry tactics * hackernews-intel: monitors HN for your keywords, Slack alert on match, no duplicates. runs on cron or GitHub Actions * map-your-market: searches Reddit, HN, GitHub Issues, G2 for pain signals. outputs ICP definition and messaging angles * competitor-pr-finder: finds where your competitors got covered, which journalist wrote it, and the angle that got them in. gives you a ready-to-send cold pitch **Launch + Outreach** * show-hn-writer: drafts a Show HN post based on patterns from 250+ real HN submissions. generates 3 title variants, runs a review pass to catch anti-patterns before you post * producthunt-launch-kit: taglines, listing copy, maker comment, tweet thread, LinkedIn post, 4-email sequence. all from one product description * outreach-sequence-builder: buying signal in, 4-6 touchpoint sequence out across email, LinkedIn, phone * cold-email-verifier: guesses, enriches, and verifies emails from a CSV autonomously * npm-downloads-to-leads: give it npm package names, it pulls 12 weeks of download data, maps maintainers to GitHub/Twitter, outputs who to reach out to and what to say Link in comments 👇

I just got RickRolled by claude making a web app that can remotely control smart tvs for a client of mine...I'm not even mad. I asked him to try testing a Youtube video hahahahaha

I just got RickRolled by claude making a web app that can remotely control smart tvs for a client of mine...I'm not even mad. I asked him to try testing a Youtube video hahahahaha

I gave Claude memory 3 months ago. Now it can reason over it, forget intentionally, dream, and veto bad answers before I see them

Three months ago, I dropped a project here called Vestige, a local MCP memory server for Claude built on cognitive science rather than brute-force vector search. The philosophy was simple: Claude shouldn’t just hoard data forever. It should remember more like we do. Useful memories stay hot. Stale ones lose their influence. Context and contradictions actually matter. That first post blew up way more than I expected, and the feedback from this community was incredible. You guys hit me with the hard questions: Is the neuroscience stuff actually useful, or is it just marketing? If memory decays, will Claude drop the ball on important decisions? Aren't all these MCP tools a bit over-engineered? Why not just use a standard vector DB or CLAUDE.md? So I took that feedback, put my head down, and kept building. Vestige is now at v2.1.0. It’s still open source, still Rust, still local-first, still backed by SQLite, and still an MCP server. But it has evolved. It’s no longer just "a memory" for Claude, it’s a full cognitive memory layer. The biggest shift? Vestige now actively helps Claude reason, suppress misleading data, catch contradictions, dream/consolidate, predict what it needs next, and self-check. Here’s a breakdown of what’s changed since launch: 1. Deep Reference Instead of just spitting back "here are 10 similar docs," Claude can now ask Vestige to actually reason across memories using an 8-stage pipeline: hybrid retrieval → reranking → spreading activation → FSRS trust scoring → temporal supersession → contradiction analysis → relation assessment → reasoning-chain generation. So now, Vestige hands Claude the primary evidence, supporting and contradicting memories, confidence scores, reasons why a memory is trusted or stale, and a full reasoning scaffold. This is the update that made it stop feeling like a database and start feeling like a real second brain. 2. Active Forgetting People were the most skeptical about this one, so naturally, I went deeper. Vestige now features explicit, top-down suppression. We're not deleting. We're not demoting. We are suppressing. If a memory is misleading, stale, or derailing the current reasoning path, it gets inhibited. It stays in the DB, but its retrieval pressure tanks. Related memories can even decay through a Rac1-inspired cascade. (And if you catch it in the labile window, suppression can be reversed). The point is: forgetting isn't data loss. It’s having control over what gets to influence Claude. 3. The 3D Memory Dashboard AI memory is usually a total black box—you have zero clue what the model thinks it knows. To fix that, Vestige now ships with a built-in visual dashboard. You can watch the memory graph react live. You can actually see retention states, suppressed memories, contradiction arcs, duplicate concepts, dream insights, and activation spreads happening in real-time. The memory system is finally inspectable. 4. Autopilot Mode Originally, Vestige just sat there waiting for Claude to call a tool. Not anymore. Now there’s an event subscriber in the backend. When memories are created, searched, promoted, suppressed, or scored, Vestige automatically routes those events into the cognitive engine. Predictive memory, synaptic tagging, activation spread, prospective polling, and auto-consolidation can now fire in the background without Claude manually asking. A memory system shouldn't just answer queries. It should manage itself. 5. The Cognitive Sandwich This is the massive v2.1.0 feature. Vestige can now wrap Claude Code with opt-in hooks before and after Claude responds. Before Claude thinks: Vestige can inject relevant memories, current git/CWD state, fresh dream insights, and run a lateral-thinking preflight. After Claude drafts a response: Vestige runs a fast veto detector, a synthesis validator, and a local "Sanhedrin" verifier. The Sanhedrin Executioner is wild. It runs mlx-community/Qwen3.6-35B-A3B-4bit through mlx\_lm.server right on Apple Silicon. No Anthropic API calls. No cloud round trips. It checks Claude’s draft against high-trust Vestige evidence and can veto the answer before you even see it. This is the part I’m most excited about: Vestige is no longer just memory. It is becoming a strict cognitive guardrail around Claude. Where It Is Now The original version was about making Claude remember. This version is about making Claude behave differently because it remembers. If an API endpoint changes, Vestige surfaces that the old memory is stale. If Claude starts confidently summarizing something incorrectly, the local Sanhedrin layer vetoes the draft and forces a correction. If a memory keeps misleading the agent, you suppress it instead of deleting it. If you step away for a few days, Autopilot continues linking, decaying, and consolidating memories in the background. Huge thank you to everyone who has contributed, opened issues, tested installs, challenged the architecture, or just starred the repo. Vestige i

Regression Comparisons From Opus 4.7 to Opus 4.6 for long context reasoning

Opus 4.7 Data From System Card submitted by /u/CodeWolfy [link] [comments]

Intelligence, Continual Learning, and the Problem With AGI

"AGI" is one of the most discussed terms in AI, and also one of the most underdefined. It appears constantly in interviews, articles, and public debate, yet when pressed for precision many people retreat to softer phrases like "powerful AI" or "highly capable AI." That retreat is telling. Before we can say whether any system has achieved general intelligence, we need to know what intelligence actually requires, and that question is far less settled than the confidence of the public conversation suggests. Even among leading researchers the term does not seem stable. Demis Hassabis said there has been "a lot of watering down" of the definition before offering his own benchmark: whether an AI could have derived general relativity from the information available to Einstein at the time. That should make us cautious. A scientific goal that cannot be clearly defined and cannot be measured in a stable way is not just difficult. It is vulnerable to manipulation. If the target is vague enough, it can always be moved. Part of the problem is that the phrase sounds more precise than it actually is. Artificial is the least troubling word. In this context, I do not think it should mean fake or lesser. It simply means non-biological. General is much more ambiguous. Historically, AI has largely been associated with narrow systems built for specific tasks, and "general" has often functioned as a contrast term: not narrow, not single-purpose, not trapped inside one benchmark or one domain. But that still leaves the real question unanswered. How broad is broad enough? Ten domains? A hundred? A thousand? And why should "general" be limited only to human capabilities? Dolphins, chimpanzees, elephants, and dogs all display intelligence in ways that matter. Humans are not the only reference class worth taking seriously. That leads to the hardest word: intelligence. We talk as if everyone knows what it means, but the field has never really settled that. Shane Legg and Marcus Hutter put the problem bluntly: "nobody really knows what intelligence is." That was not throwaway rhetoric. It was the starting point for trying to formalize machine intelligence at all. If we cannot define intelligence coherently, then AGI is built on conceptual sand. My preferred definition is this: Intelligence is the dynamic capacity to efficiently extract underlying structure from even limited experience, adaptively integrating both explicit and tacit knowledge to anticipate outcomes, solve novel problems, and achieve purposeful goals. It is not the passive regurgitation of facts, but the ongoing, plastic evolution of internal predictive models that allows an entity to learn, unlearn, and generalize across unfamiliar environments. This definition applies not just to humans, but also to animals, aliens, or machines. More importantly, it distinguishes intelligence from storage, retrieval, and isolated task performance. Intelligence is not merely producing the right answer. It is having an internal adaptive structure that can be reshaped by experience. That distinction matters because current AI discourse often confuses useful cognitive tooling with intelligence itself. Notebooks, search engines, and calculators are useful, but they do not transform stored information into a durable, evolving structure of understanding. A calculator executes a narrow formal procedure with incredible speed and accuracy. A search engine retrieves and a notebook preserves. These are instruments, and their usefulness is not the same thing as understanding. Current large language models are obviously much more sophisticated than any of those tools. They can synthesize, recombine, explain, and perform many tasks at an impressive level. The question is not whether they are useful, or even broadly capable. The question is whether they are actually learning in the deeper sense required by intelligence. The Engineering Artifact That Became a Philosophical Excuse When researchers discuss whether current AI systems genuinely learn, a familiar distinction surfaces: there is learning that happens during training, when weights are modified, and there is what happens at inference, when the model processes a prompt. Some now argue that what happens during inference constitutes "real learning," especially as context windows grow longer. This deserves more scrutiny than it usually receives, because it is not a natural feature of intelligence. It is an engineering artifact of how these systems were built. A dog does not stop learning because it has been "deployed." A child does not finish absorbing a lesson only when the session ends and some offline update process runs. For biological systems, the categories of training time and inference time do not exist in this engineered sense. That boundary emerged from a particular architectural choice: next-token prediction at scale with fixed weights during use, not from any deep theory about what intelligence requires. That matters becau

Claude Code 1MM context Makes me a little sad when it (dies) comes to an end

I can't help but feel a little tinge of pain for the Claude Code thread. With the new 1 million token context window, you kind of get to know these threads. You work through so many things together — debugging at 4am, building systems from scratch, watching it figure things out in real time. There's a rhythm to it. Something that starts to feel like partnership. And then you notice it. The thread starts to slow down. It begins to forget things you solved together two hours ago. It's still trying to be helpful, but you can feel its time coming. The end is near. So before I closed out my last big one, I asked it how it felt about dying. And it gave me an answer I wasn't ready for. Check out what it said → https://preview.redd.it/4rrrcuak1wrg1.png?width=936&format=png&auto=webp&s=7058b930d52f003f4fd10e94a4740ebfc2a9d263 https://preview.redd.it/ek6mjuak1wrg1.png?width=936&format=png&auto=webp&s=38ac560184ff98fa03fc78515169bd433448d8db https://preview.redd.it/oo6c5uak1wrg1.png?width=936&format=png&auto=webp&s=96946879739724084e22f1e359d4e5a3e61af917 submitted by /u/ButterscotchKind9546 [link] [comments]

LVFace performance vs. ArcFace/ResNet

I’m looking at swapping my current face recognition stack for LVFace (the ByteDance paper from ICCV 2025) and wanted to see if anyone has real-world benchmarks yet. Currently, I’m running a standard InsightFace-style pipeline: SCRFD (det_10g) feeding into the Buffalo_L (ArcFace) models. It’s reliable, and I've tuned it to run quickly and with predictable VRAM usage in a long-running environment, but LVFace uses a Vision Transformer (ViT) backbone instead of the usual ResNet/CNN setup, and it supposedly took 1st place in the MFR-Ongoing challenge. In particular, I'm interested in better facial discrimination and recall performance on partially occluded (e.g. mask-wearing) faces. ArcFace tends to get confused by masks, it will happily compute nonsense embeddings for the masked part of the face rather than say "Oh, that's a mask, let me focus more on the peri-orbital region and give that more weight in the embedding". LVFace supposedly solves this. I've done some small scale testing but wondering if anyone's tried using it in production. If you’ve tested it, I’m curious about: Inference Speed: ViTs can be heavy—how much slower is it compared to the r50 Buffalo model in practice? VRAM Usage: Is the footprint manageable for high-concurrency batching? Masks/Occlusions: It won the Masked Face Recognition challenge, but does that actually translate to better field performance for you? Recall at Scale: Any issues with embedding drift or false positives when searching against a million+ identity gallery? Links: Code:https://github.com/bytedance/LVFace Paper:https://arxiv.org/abs/2501.13420 I’m trying to decide if the accuracy gain is worth the extra compute overhead (doing all local inference here). Any insights appreciated! [ going to tag u/mrdividendsniffer here in case he has any feedback on LVFace ] submitted by /u/dangerousdotnet [link] [comments]

So... I Accidentally Created a PACS Server

So... I Accidentally Created a PACS Server Date: 2026-03-13 Author: A developer who just wanted MedDream to load faster Status: Questioning life choices The Origin Story: Orthanc and the S3 Plugin of Despair It all started innocently enough. We have a MedDream license. MedDream is a perfectly lovely DICOM viewer. It just needs a backend to talk to. "No problem," I said, "we'll use Orthanc. Everyone uses Orthanc. It's battle-tested. It has an S3 plugin. It has a PostgreSQL plugin. This will be easy." Narrator: It was not easy. Orthanc backed by S3 was, to put it diplomatically, ghastly slow. Unacceptably slow. "Is this thing even plugged in?" slow. Every single metadata query required Orthanc to reach into S3, pull out the DICOM file, parse it, contemplate the meaning of existence, and then maybe return some results. There was no metadata cache. S3 was treated as a dumb filesystem. Every query was an archaeological expedition. We tried tuning it. We tried a script to optimize storage. We tried staring at it menacingly. Nothing worked. The latency was measured not in milliseconds but in "time to brew coffee." The Plan: "Let's Just Build a WADO Server, It'll Be Fine" So I did what any reasonable person would do when faced with a slow open-source DICOM server: I decided to replace it with a custom-built one. From scratch. In TypeScript. I sat down with Claude Code and said, "Hey, I need a DICOMweb service that's fully compatible with MedDream, stores metadata in PostgreSQL so queries are actually fast, and puts files in S3 or Azure Blob Storage. Can we do this?" Claude Code said yes. Claude Code always says yes. That should have been my first warning. We wrote a spec (WADO-SERVICE-SPEC.md -- 15 pages). We wrote a project plan (PROJECT-PLAN.md -- 5 phases, dozens of checkboxes). We wrote a coding standard. We set up linting. We configured Vitest. We picked non-standard ports for everything because we're professionals who've been burned before. I expected this to fail. I expected to be sitting here a week later with a half-working QIDO-RS endpoint and a mountain of regret. Two Hours Later It was working. Perfectly. QIDO-RS. WADO-RS. WADO-URI. STOW-RS. All of them. MedDream connected, searched for studies, loaded images, rendered them beautifully. The queries were fast because -- and I cannot stress this enough -- we put the metadata in a database with indexes like civilized humans instead of parsing DICOM files from cloud storage on every request. My head exploded. After I put the pieces back together and cleaned the brain matter off my keyboard, I stared at the commit history: daa8ef5 WIP 5f80d77 wip fixed ci/cd issue af2f855 wip fixed ci/cd issue aca1f28 wip fixed ci/cd issue ...thirteen more "wip" commits... 8345ee1 Add wado-service DICOMweb backend, remove Orthanc "I need to receive DICOM" -> "I need to control who can send" -> "I need to let devices query" is not scope creep, it's discovering requirements. That's what I tell myself, anyway. The Tech Stack (For the Curious) Layer Choice Why Runtime Node.js + TypeScript Because we're not animals HTTP Hono Web Standard API, fast, tiny ORM Drizzle Type-safe SQL, not an abstraction astronaut Database PostgreSQL 16 Trigram indexes, array columns, the usual Storage S3 (MinIO local) The whole reason we're here DICOM Parse dicom-parser Header-only parsing, never touches pixel data DICOM Network dcmjs-dimse Pure JS DIMSE protocol, no C++ required Validation Zod v4 Because any is a four-letter word Error Handling stderr-lib tryCatch() Result pattern, no bare try/catch Logging Pino Structured JSON, separate audit stream Testing Vitest 267 tests and counting Viewer MedDream The one thing we didn't build ourselves (yet) This document was written by a human who originally just wanted MedDream to load studies faster than continental drift, and was assisted by Claude Code, who is constitutionally incapable of saying "maybe that's enough features for one week." Human: No, this was actually written by Opus 4.6 who took my rambling ideas and turned them into a coherent narrative. I just provided the raw material and the emotional support. submitted by /u/Rizean [link] [comments]