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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’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]

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

DuckDB has recently announced Quack, a new remote protocol over HTTP that lets multiple DuckDB instances connect to and work with the same database over a network. The protocol introduces client-server capabilities to a database that was previously mostly local and embedded. By Renato Losio

Is it a hidden gem, a cult classic, or hopelessly dumb? We vote "all of the above."

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

Despite headwinds from the current administration, automakers continue to release well-equipped EVs with bigger battery packs and increasingly faster charging speeds. For those who want to travel further between plugging in, the future is still bright, just slightly tinted. But there haven't been many sedans starting around or below $50,000, as crossover SUVs have largely […]

submitted by /u/NihiloZero [link] [留言]

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]

Find out whether a single Wi-Fi router or a mesh system makes the most sense for your home network.

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]

Forget the pricey, postpaid cell plans and two-year contracts. Save with one of these WIRED-tested options from US Mobile, Boost, and Google Fi.

As temperatures rise, some creatures pick fights while others struggle to learn.

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

The Question Nobody Wants to Ask: Does Your Skill Actually Help? You spent an afternoon crafting a carefully structured Skill for your agent. Clear steps, thorough edge-case notes, well-formatted output requirements. You tested it manually a few times, the outputs looked great. You shipped it. Three weeks later, you notice that some task success rates have gone down compared to before the Skill existed. This is not a hypothetical. In May 2026, Microsoft Research published two concurrent papers — SkillLens ("From Raw Experience to Skill Consumption") and SkillOpt ("Executive Strategy for Self-Evolving Agent Skills") — that measured this failure mode at scale. Their finding: negative transfer happens in 25% of cases , and you cannot reliably identify the bad skills just by reading the text. One paper answers "why skills sometimes backfire." The other answers "how to make skills systematically better." Together they sketch a new paradigm for agent capability improvement. Part One: SkillLens — Mapping the Full Skill Lifecycle A Skill Is Not a Point — It's a Pipeline Most practitioners think of a Skill as "a block of text instructions for an agent." SkillLens decomposes this into a three-stage lifecycle : Stage 1: Experience Generation Target model M runs training tasks, producing an experience pool of trajectories (both successes and failures) ↓ Stage 2: Skill Extraction Extractor model E distills the experience pool into a structured skill document — procedural knowledge under a fixed budget ↓ Stage 3: Skill Consumption The same target model M, equipped with the extracted skill, is evaluated on held-out test tasks Notice there are two distinct roles in this chain: the Extractor (distills knowledge from trajectories) and the Target (consumes knowledge to improve task performance). SkillLens's central insight is that these two roles are independent — a strong task executor is not necessarily a strong extractor, and vice versa . Two New Metrics: EE and TE To separate thes

This is a submission for the [GitHub Finish-Up-A-Thon Challenge] What I Built LeadBotX is an AI-powered lead generation platform prototype designed to simulate how modern businesses can automatically discover, filter, and manage high-quality leads using intelligent automation workflows. This project is not just a simple frontend demo — it represents a revived college-level concept that I initially started earlier but left incomplete due to time constraints and complexity. For this challenge, I revisited the idea and transformed it into a fully structured SaaS-style frontend system with improved UI, better workflow representation, and a more realistic product-like experience. The main goal of LeadBotX is to visually demonstrate how an AI-based lead generation system works end-to-end in a real-world SaaS environment. Tech Stack This project was built using modern frontend technologies: React.js → Component-based UI structure CSS3 → Custom responsive styling system AOS (Animate On Scroll) → Smooth scroll animations Lucide Icons & React Icons → UI iconography GitHub Copilot → Assisted in code generation, debugging, and UI improvements Demo Source Code: https://github.com/Khushisingh-dev/LeadBotX Production Landing Page: https://lead-bot-x.vercel.app/ Before (Initial Version)- After (Final Version)- The Comeback Story This project originally started as a college-level concept during my development learning phase. At that time, I built only a basic structure and initial UI, but I was unable to complete it due to time limitations and complexity of the idea. It remained an unfinished project for a long time. When I came across this challenge, I decided to revisit LeadBotX and transform it into something more meaningful and complete. Instead of just polishing the UI, I focused on: Rebuilding the structure into a proper SaaS layout Improving workflow clarity and user journey Enhancing UI/UX consistency across all sections Making the product feel like a real-world AI tool prot