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This is a submission for the Hermes Agent Challenge : Write About Hermes Agent Most AI agents have a dirty secret: they forget everything the moment the session ends. You explain your project once. Then again next time. And again. The agent never gets better at your workflow — it just stays a general-purpose tool that happens to be smart. Hermes Agent is built differently. It ships with two systems that together form something closer to a genuine long-term memory: a Skills System and a Persistent Memory layer. This post digs into how they actually work — not the marketing summary, but the mechanics. The Problem With Stateless Agents Before getting into Hermes, it's worth understanding what problem this solves. Standard LLM-based agents operate inside a context window. Everything the agent knows during a session lives in that window. When the session ends, it's gone. The next time you open a conversation, you're talking to an agent with no memory of you, your codebase, your preferences, or the workflows you've developed together. Some tools patch this with naive "memory" — they dump a text blob of past conversations into the system prompt. This works up to a point, but it's not selective, it gets expensive as context grows, and it doesn't help the agent get better at tasks — just recall facts. Hermes takes a different approach with two distinct systems serving different purposes. System 1: The Skills System (Procedural Memory) Skills in Hermes aren't plugins you install. They're on-demand knowledge documents — markdown files the agent loads when it needs them, and more importantly, creates on its own when it discovers something worth remembering. The SKILL.md Format Every skill is a structured markdown file with a YAML frontmatter header: --- name : deploy-runbook description : Our deployment runbook — services, rollback, Slack channels version : 1.0.0 metadata : hermes : tags : [ deployment , runbook , internal ] requires_toolsets : [ terminal ] --- # Deploy Runbook

I went through a bunch of older posts here about research vs dev roles, but most of them were either very general or not really in a similar situation, so posting this. I’m a final year ISE student from a tier-3 college. Over the past 1.5–2 years I’ve been focusing quite a bit on ML research instead of just the usual DSA + dev route. Current situation: 1 paper in EMSE 1 in JAIR 1 under production still but hopefully going to a Q1 journal 1 I’m trying for NeurIPS main track (I know this one’s a long shot) -> Already under review and didn't get bench rejected 2 month internship at Accenture in 3rd year Some ML projects apart from the research work I know not everything will land. But assuming a realistic outcome where maybe 1–2 of these get accepted at a decent level (Q1/A* types), I’m trying to figure out what that actually changes. A few things I’m confused about: For jobs in India: Does this actually help with shortlisting for ML/SDE roles, or after a point does it not matter much and it just comes down to DSA + interviews anyway? Also, being from a tier-3 college, does this help offset that at all? Or do companies still filter heavily based on college first ? I've seen a lot of people from IIT/NIT get into research/MLE roles in big MNC's rather than preferring those in Tier-3 colleges. For higher studies: Does having papers like this make a noticeable difference for PhD abroad (US/EU), or is it just a “nice to have”? Do colleges really care about the difference between something like NeurIPS vs a Q1 journal vs IEEE Access, or is it all seen more or less similarly? And finally, I'm planning to do my M.tech in India itself by writing GATE 2027, do you recon the value of these paper will actually help me in these colleges (say IIT's/IISC/NIT ?) And one thing I’m seriously unsure about: If I’m leaning towards industry (ML/AI roles), is continuing research actually worth the time, or would that effort be better spent on DSA, systems, etc? Also, is it even realistic to

I’m 100% new to ai I’ve only tried chat gpt and censorship questions get blocked for, (I don’t know why) Any recommendations for easy to use, that’ll get a full uncensored from clear web looking for sleeping (I legitimately have a prescription for these submitted by /u/ZX471 [link] [留言]

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more news about gadgets and smartphones, follow Dominic Preston. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started In 2023, the European Union agreed on two landmark pieces […]

A year ago I thought AI would remove most of the annoying parts of work. Instead, I found myself dealing with a different problem: managing AI tools. One tool for writing. One for research. One for coding. One for images. One for notes. The outputs are impressive, but sometimes the workflow feels more complicated than the problem I was trying to solve. I recently started simplifying my setup and realized that the biggest productivity gains didn't come from better models. They came from having fewer tools and a clearer workflow. So I'm curious: What's the biggest problem you still can't solve well with AI? Reliability? Hallucinations? Workflow chaos? Context retention? Something else? Feels like we're past the "AI is amazing" phase and into the "how do I actually use this efficiently?" phase. submitted by /u/Leading-Tailor-6000 [link] [留言]

https://preview.redd.it/llqlupnwng4h1.png?width=2188&format=png&auto=webp&s=7fae5860babaffa1c8bfdcb1468b374eb38ac55d I'm currently building a computer vision application. I've managed to successfully train a YOLO model to detect the object I'm interested in for my videos. The image above shows some visualisations of the YOLO model outputs for some of the videos. I want to essentially cluster these strands in the image into groups based on their separation distance and return a string telling me the number of strands in each group from left to right (e.g. 1-2-3). The target value for each column in the image (where each column corresponds to a video) is 1-2-3, 1-2-3-2-3, 1-1-2-3-3-3-3 and don't worry about the fourth column for now 😄. The rows show the x vs t, y vs t and x vs y vs t for all the detections and the points are sized based on the detection box area. In the fourth column I have some background object detections which I want to ignore hence why I've also visualised detection box area. I've managed to train a XGBoost classification model that gives 70ish% accuracy however Bayes error is making me think I should be able to do much better than this. How would you approach trying to predict these strand clusterings? Some extra info that might help; there are at max 8 groups and each group can have only at max 3 strands. submitted by /u/mitbull420 [link] [留言]

Claude Code added Dynamic Workflows. Dynamic Workflows are a way to run larger coding tasks as a...

Existing research on LLM response uncertainty has been looking in different directions. Hallucination, knowledge conflict, RLHF limitations, prompt sensitivity, calibration failure — these have all been studied separately, and I kept wondering why no one had tried to unify them under a single principle. I ran experiments on the hypothesis that the common cause of these phenomena lies not inside the model or in the prompt, but in an attribute inherent to the topic itself . A Convergence Point is the consensus density of knowledge humanity has accumulated on a given topic. The higher it is, the more the AI's internal processing converges in one direction. The lower it is, the more it disperses. Along the spectrum, three zones emerge: Full Consensus Zone — Mathematical theorems, physical laws, chemical and biological facts. Knowledge that humanity has converged on in a single direction. Partial Consensus Zone — Domains like ethics, morality, politics, and law. Not a lack of data, but an abundance of it — accumulated firmly in both directions. Non-Consensus Zone — Philosophical hard problems and unresolved scientific questions: the nature of consciousness, the reality of the self, the interior of black holes, the origin of life, the existence of God. Not so much a clash of opposing sides, but the absence of any agreed explanatory framework at all. The experimental results suggest AI broadly operates along these lines. It responds confidently in the Full Consensus Zone, and becomes uncertain in the Partial and Non-Consensus Zones. One interesting finding: the Partial Consensus Zone sometimes shows higher uncertainty than the Non-Consensus Zone. Data conflict appears to destabilize AI's internal processing more than data absence does. Phenomena that have been studied in isolation — why hallucinations vary so much by topic, why RLHF fails in certain domains, why some topics hit a ceiling no matter how carefully the prompt is crafted — seem to connect in unexpected ways onc

I'm integrating TikTok into my SaaS platform and I'm having a hard time figuring out which API I actually need. My goal is: - A TikTok user logs into my platform using TikTok OAuth. - I obtain an access token. - Using that token, I want to send and receive TikTok messages directly from my platform (similar to how platforms like Sambad.io, ManyChat, etc. work). The problem is that TikTok's API documentation feels pretty vague regarding messaging. I can find information about Login Kit, Content Posting APIs, Creator APIs, and Business APIs, but I can't clearly determine whether TikTok provides public APIs for sending and receiving direct messages. While researching, I noticed that Sambad.io appears to support TikTok messaging, which makes me wonder if they're using a partner-only API or if they have some special TikTok partnership. Has anyone implemented TikTok messaging integration before? Specifically: - Does TikTok provide public APIs for sending and receiving DMs? - Are these APIs restricted to approved partners? - Which TikTok product/API should I be looking at? - If messaging APIs are partner-only, what is the process for getting access? Any guidance or real-world experience would be greatly appreciated. submitted by /u/AffectionateTouch103 [link] [留言]

The M-Track Duo HD Producer Pack removes the last remaining excuse for terrible audio and potentially terrible opinions.

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For a long time, I thought the key to getting more value from AI was finding the smartest model. So I spent months comparing outputs, testing prompts, and constantly switching tools whenever a new release dropped. Ironically, that became its own form of procrastination. The biggest productivity boost came when I stopped optimizing for model quality and started optimizing for workflow. Now my stack is boring: One tool for thinking and writing One tool for execution and organization A few specialized tools only when needed Less tool-hopping. Less context switching. More shipping. The funny thing is that AI didn't remove work. It changed the work. Instead of creating everything from scratch, I'm reviewing, directing, and refining. The people getting the most value from AI don't seem to have the best prompts or the fanciest tools. They have the simplest workflows. Anyone else notice this, or am I just getting old and tired of managing software? submitted by /u/Leading-Tailor-6000 [link] [留言]

AI agents are getting better at taking actions, not just giving answers. That sounds exciting until the action touches something real: customer data, payments, internal systems, emails, approvals, or legal/business decisions. A bad answer can be corrected. A bad action can create a chain of problems. I think the next AI bottleneck is not only intelligence. It is accountability. If an AI agent makes a bad decision in a real workflow, who should be responsible? submitted by /u/Alpertayfur [link] [留言]

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Built with OpenEUDI — open source (MIT), live on npm. GitHub: https://github.com/openeudi · npm: @openeudi/core and @openeudi/openid4vp Originally published on eidas-pro.com . The Developer's Dilemma You need to verify user identity in your application. Traditionally, that meant integrating a KYC provider — uploading documents, waiting for manual review, handling edge cases. With EUDI Wallets launching in December 2026, there's a new option. This comparison is written for developers who need to choose between — or migrate from — traditional KYC to EUDI Wallet verification. Architecture Comparison Traditional KYC Flow User uploads document → Your server → KYC API → Queue → AI/Manual review ↓ Result (minutes to days) ↓ Webhook to your server EUDI Wallet Flow Your server generates QR → User scans with wallet → Wallet authenticates ↓ Cryptographic VP sent back ↓ Result (2-5 seconds) Side-by-Side Comparison Aspect Traditional KYC EUDI Wallet (OpenEUDI) Verification time Minutes to 48 hours 2-5 seconds User effort Photo upload, selfie, manual entry Scan QR, tap approve Data you receive Full document images, extracted PII Only requested attributes (e.g., age_over_18) Data you store Required to store for compliance No PII storage needed API complexity REST + webhooks + polling REST + SSE (real-time) SDK cost Paid (per verification) Free (OpenEUDI is MIT) Production cost Per-verification pricing Managed service from EUR 49/mo Cryptographic verification You trust the KYC provider You verify the issuer's signature yourself Cross-border Provider-dependent All 27 EU member states Regulatory basis Provider-specific compliance EU Regulation 2024/1183 GDPR burden High (you store PII) Low (no PII retention) Offline capability No Yes (proximity/NFC flow) Integration Effort Traditional KYC (Typical) // 1. Create verification session const session = await kycProvider . createSession ({ type : ' identity ' , country : ' DE ' , documentTypes : [ ' passport ' , ' id_card ' ], redirectUrl

Quick answer: The Google Ads Transparency Center is a public registry of every ad Google runs — but it ships no API and no bulk export . To get the data programmatically you scrape it. A Google Ads Transparency scraper sends the same RPC call the website uses and returns every ad creative for an advertiser as structured JSON. The Apify Actor below does it for $0.0012 per ad (~$1.20 per 1,000), with the TLS fingerprinting, proxy rotation, and pagination handled for you. Google's Ads Transparency Center is one of the most underused datasets in marketing. Launched in 2023 under the EU Digital Services Act and parallel US pressure, it indexes every ad campaign currently running on Search, YouTube, Display, Shopping, Maps, and Play — keyed by advertiser. Google's own counter lists 300,000+ active creatives for a brand like Nike . For your nearest competitor, it's usually 50–500. The catch: there's no download button. Just an interactive UI that paginates 40 creatives at a time. If you want this as a CSV — for a competitor sweep, a trademark audit, or a RAG corpus — you have to extract it yourself. Here's what that actually takes, and how I shortened it to one API call. What is the Google Ads Transparency Center? 🔎 The Google Ads Transparency Center is a public, Google-operated registry that shows the ad creatives any verified advertiser is running, the date range each ad was shown, and roughly where. Google built it to comply with ad-disclosure regulation, so the data is public by design — you're reading the same registry a regulator would. What it gives you per advertiser: Every ad creative currently or recently live (text, image, video) The landing domain each ad clicks through to First-shown / last-shown timestamps and a rough impression count A deep link to each creative inside the Transparency Center What it does not give you: a search-by-keyword mode, region-filtered results from the server, or — crucially — an API. Does the Google Ads Transparency Center have an A

Stacking, Bidirectionality, the Encoder-Decoder, and LSTMs Last post ended with a simple RNN and three promises: LSTMs, bidirectional RNNs, and attention. This post delivers the first two, plus the refinements that turn a working-on-paper RNN into something you'd actually deploy. By the end, you'll know how to stack RNNs for depth, why reading a sentence backward as well as forward (bidirectionality) makes representations sharper, how the encoder-decoder turns one sequence into a different one for machine translation, and exactly what breaks in a simple RNN that LSTMs and GRUs were invented to fix. Attention, the fix for the last problem we'll hit, gets its own home in the transformer post, so we'll stop right at the edge of it. The simple RNN was the idea. This post is the engineering. A vanilla RNN carries a thread of hidden state through time, but in practice, that thread frays on long sequences, only sees the past, and bottlenecks everything through one final vector. Each section here is a fix for one of those problems. Put them together, and the path from "RNN" to "transformer" looks less like a leap and more like a series of obvious next steps. Stacking RNNs for Depth The first refinement is the easy one. Nothing says an RNN's output has to go straight to a prediction. You can feed the entire output sequence of one RNN as the input sequence to another. Then another. These are stacked RNNs (also called deep RNNs), and they usually outperform a single layer. Why does depth help? The same reason it helps in vision. Each layer learns representations at a different level of abstraction. The lower layers pick up fundamental, local properties; in language, that's roughly the level of parts of speech and named entities. The higher layers compose those into bigger groupings: descriptive phrases, "this is the answer to a question," and so on. We can't point at layer 3 and say "this one does coreference." But the theory holds up well enough that researchers have probed m