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This is a submission for the Hermes Agent Challenge Agentic Web3: Automating Blockchain Workflows with Hermes Tags: #hermesagentchallenge , #web3 , #agents , #solana The blockchain industry has spent the last decade building decentralized, permissionless infrastructure. However, the user experience layer interacting with this infrastructure remains overwhelmingly manual. Decentralized applications (dApps) require users to constantly monitor markets, parse complex data, and manually sign every transaction. The next evolution of Web3 isn't just about faster blockchains; it is about autonomous execution. By integrating large language models and agentic frameworks with smart contracts, we can transition from a paradigm of manual execution to intent-based autonomy . In this article, we will explore how to bridge the gap between AI and decentralized networks by automating blockchain workflows using the Hermes Agent framework. We will look at the architecture of an on-chain agent, how it reads and writes to a network, and how high-performance environments like Solana are making these agentic experiences viable. The Paradigm Shift: From Passive Wallets to Active Agents Currently, most AI in Web3 is limited to read-only analytical tools—chatbots that can summarize a smart contract or pull token prices from an API. While useful, these are fundamentally passive systems. An active agent is different. Powered by a framework like Hermes Agent, an active agent can: Observe: Continuously monitor on-chain events via RPC nodes or webhooks. Reason: Use its LLM core to interpret those events against a set of user-defined goals or risk parameters. Act: Formulate a transaction, sign it via a secure wallet environment, and broadcast it to the network. This opens up massive possibilities. Imagine an agent that automatically manages your decentralized finance (DeFi) positions, rebalancing a portfolio based on yield changes across different protocols. Or consider fully on-chain gaming, where

Six months into running my SaaS, my "feedback system" was three browser tabs, a starred Gmail folder, and a sticky note on my monitor that said "check Discord." That was the whole system. It held together until the day I found a three-paragraph email from a paying user — a genuinely detailed feature request with a real use case — sitting unread for 24 days. His last line was: "Happy to pay more if you can support this." I replied the same afternoon I found it. His reply: "Switched last week, thanks anyway." That was the moment I stopped treating feedback management as a nice-to-have. Why the usual fixes didn't fix anything I tried the obvious things first. I want to document them because I see a lot of people cycling through the same failed solutions. Notion database 🪦 Built a beautiful one. Color-coded tags, priority columns, status tracking. It lasted 11 days before nobody — including me — was maintaining it. The friction of "open Notion, find the right database, fill in six fields" is invisible when you're designing the system and fatal when you're in the middle of a support conversation. Airtable form 🪦 Better entry point, still disconnected from where users actually were when they had feedback. Nobody bookmarks your Airtable form. They DM you on Discord and you think "I'll add that later" and you don't. Canny — this one actually worked, for a while I genuinely liked Canny. Clean interface, users could upvote requests, I could see what was popular. It felt like a real system. Then our user count grew and the pricing tier jumped. I was looking at $99/month for a feedback board for a product still finding its footing. That's not a moral judgment on Canny — it's a fair product — but for a bootstrapped indie dev, it started feeling like a tax on momentum. The deeper problem with all three solutions was the same: they were inboxes, not loops. User submits → enters the void → user never knows if anyone saw it → user assumes nobody did → trust erodes → churn. I had bui

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

Canvas first AI agent harness. MCP native. Local first. Discussion | Link

Everyone knows about tech debt. You cut corners on code quality to ship faster, and you pay for it later. We're definitely watching a new version of that emerge in real time, except instead of deferring manageable code, you're deferring actual understanding. And unlike tech debt, cognitive debt compounds invisibly. You don't get a failing test suite. You just get someone who can't debug their own project, can't evaluate whether the AI's suggestion is good, and can't extend what they've built without prompting their way through it again. What I keep thinking about is where this leads at scale. Right now it's mostly developers vibe-coding their way through projects they half-understand. But AI is moving into law, medicine, and finance. The same dynamic follows: people making consequential decisions with tools they can't interrogate, in domains where "I'll just re-prompt it" isn't a recovery strategy. The pessimistic, or maybe rational read is that judgment without foundational understanding is just confident ignorance, and we're building entire careers on that foundation right now. Curious what people here think. Does cognitive debt get self-correcting as the stakes get high enough? Or are we sleepwalking into a generation of professionals who are deeply dependent on systems they fundamentally don't understand? submitted by /u/Expensive_Trouble_40 [link] [留言]

Hey everyone, I'm trying to get back into the loop on world models. The last time I followed SSL closely, the buzz was all about Barlow Twins and DINO, but now everything just looks like scaled-up video generation from big industry labs. What is the actual academic research community stressing over right now? submitted by /u/nat-abhishek [link] [留言]

okay so this is embarrassing to admit but here it is took a reasoning test in 2022, scored pretty well. Retook the same test last month out of curiosity, dropped significantly, like not a small difference. The only major change in my life is using AI tools daily for work and the worst part? i kind of knew something was off before the test. I noticed i couldn't sit with a problem anymore without immediately opening chatgpt, like my brain forgot how to be uncomfortable for even 5 minutes memory is worse. attention is worse, i feel slower in conversations. but my productivity at work has never been higher lol so what is actually happening here , are we trading long term cognitive health for short term output? Has anyone else noticed this or is it just me being paranoid ⊙⁠﹏⁠⊙ genuinely asking because i don't want to just accept this as normal (⁠｡⁠ŏ⁠﹏⁠ŏ⁠) submitted by /u/Difficult-You9582 [link] [留言]

Hi, I'm Brazilian and I'm a web developer. I'm eager to start a project (without involving money at the moment). I'd like to start developing something together. I've been working in this field for a while now, I have several projects underway, and I'd like to start another one with someone.If anyone is interested, I can give more details about myself and some ideas I've had in private 🙂 I think two people working together learn more and wouldn't spend anything; it would only involve money for selling the website submitted by /u/BitesBitesou [link] [留言]

The Logging Nightmare Deploying microservices across Multi-Cloud environments using tools like Terraform is an exhilarating milestone. But the moment something breaks, that excitement quickly turns into a nightmare. The SSH Grind : If you find yourself SSH-ing into disparate instances just to run tail -f and grep through scattered log files, you're doing it wrong. The Agnostic Approach : The industry standard demands Centralized Logging, but chaining your application to vendor-specific solutions like AWS CloudWatch or GCP Cloud Logging limits your architectural freedom. Implementing a true "Cloud-Agnostic" ELK stack gives you back control over your observability data. Clean Architecture & The Non-Blocking Logger Factory Building this robust observability pipeline requires adhering to Clean Architecture principles, specifically through a Non-Blocking Logger Factory. Standardized Interface : By wrapping modern logging libraries like Winston or Pino , we standardize our application's logging interface. The Secret Sauce : The winston-elasticsearch transport module buffers your logs and pushes them directly to your Elasticsearch cluster in the background. Non-Blocking : This architectural choice is crucial: it ensures that high-volume log streaming happens without blocking the Node.js event loop . Here is how the data flows through the system: Resilience Fallback (The Failsafe) A centralized system introduces a dangerous dependency. Your logging infrastructure must never be the reason your application crashes. The Threat : If the remote Elasticsearch cluster is unreachable due to network partitions or rate limits, a poorly configured logger will throw uncaught exceptions, bringing down the app. The Solution : We implement a strict Resilience Fallback (Failsafe) mechanism. The transport module safely catches the connection errors and seamlessly falls back to standard output (console), guaranteeing continuous operation. The 512MB Local-Test Challenge While this setup is a

A stack de autenticação em .NET fica sólida quando separamos duas responsabilidades: ✅ Argon2id para guardar senhas (hash irreversível, lento, memória-intensivo) ✅ JWT Bearer para provar identidade depois do login ✅ Validação de iss , aud , exp e assinatura em cada request ✅ Segredos fora do repositório (ambiente / Key Vault) Se o ecossistema .NET já oferece hosting, APIs e pacotes maduros, combinar Argon2 (referência da Password Hashing Competition , testável em argon2.online ) com JWT é o caminho natural para microsserviços e Web APIs. Neste artigo, mostro o fluxo registo → login → token → rotas protegidas com foco no que implementar no dia a dia. ⚠️ Observação importante JWT não substitui Argon2. Nunca coloque senha ou hash no payload do token. Argon2 protege a credencial na base de dados; JWT é sessão assinada com expiração. 🧠 Visão Geral Aspecto Argon2 (senha) JWT (sessão) Foco Resistir a offline cracking Autorizar requests após login Onde vive Coluna password_hash na BD Header Authorization: Bearer Algoritmo Argon2id (OWASP) HMAC-SHA256 ou RSA (config) Ferramenta de estudo argon2.online docs Microsoft JWT Bearer Runtime Biblioteca .NET (ex.: Konscious Argon2) Microsoft.AspNetCore.Authentication.JwtBearer Erro clássico MD5/SHA rápido na senha Token sem validar aud / iss 🧩 O que o Argon2 resolve (camada 1) O Argon2 é o vencedor da Password Hashing Competition — hoje a referência para novas passwords . 1️⃣ Hash irreversível com Argon2id var hash = hasher . Hash ( password ); await store . CreateAsync ( email , hash ); ✅ Salt único por utilizador ✅ Parâmetros m , t , p documentados no próprio hash ✅ Verificação com tempo constante ( FixedTimeEquals ) 2️⃣ Calibrar custo com consciência Em argon2.online podes experimentar memory cost e iterations — útil em laboratório. 📌 Em produção usa biblioteca auditada (.NET), não hashes de utilizadores reais em sites públicos. 3️⃣ O que não fazer na senha ✅ Não “criptografar” senha com AES reversível ✅ Não MD5 / SHA-1 / SHA-256

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built VKara is a browser-based karaoke room app for singing at home with friends or family. It is not trying to replace YouTube. YouTube is already great at playing videos. It already has almost every karaoke song we need. But YouTube is not really designed to manage a karaoke night where many people want to choose songs together. That is the gap VKara tries to fill. You open VKara on a TV or laptop as the main playback screen. Everyone else joins the same room from their phone using a 4-digit room code or QR code. Then anyone can search for songs, add them to the queue, pause, resume, or skip. The TV only needs to play the video. Everyone's phone becomes their own remote. That is the whole idea. Simple enough to explain in one sentence. Not simple enough to build in one weekend. I learned that part the hard way. Demo Links: Live demo: https://vkara.vercel.app/en GitHub repo: https://github.com/lehuygiang28/vkara Before branch: https://github.com/lehuygiang28/vkara/tree/before Old backend repo: https://github.com/lehuygiang28/vkara-api Small warning: the demo is running on limited resources, so if it is slow, please give it a moment. My wallet is still a student wallet. lol. The flow is: Open VKara on a TV or laptop. Join the room from a phone by code or QR. Search for a karaoke video. Add it to the shared queue. Control playback together. Before: the idea worked, but the product still felt like a video app squeezed into a karaoke use case. After: the mobile flow is now focused on joining, searching, choosing an action, and controlling playback. The Comeback Story I started VKara around early 2025. At that time, my goal was very personal. I wanted a better way to sing karaoke at home with friends. The normal setup was: open YouTube on a TV, search for karaoke videos, and pass control around. It worked, but it was awkward. One person was searching. Another person accidentally played a video immedia

He's been on the same exestencial crisis for a while so how do I end it submitted by /u/Huge_Heart3957 [link] [留言]

There is a specific failure mode in AI-assisted QA work that most tooling discussions skip entirely, and it shows up earliest when you are working solo on a real engagement. Every new chat session is stateless. You paste the ticket, describe the feature, explain your severity logic, set up the context, and by the time the AI is actually useful, you have rebuilt your methodology from scratch for the third time that week. That is not a workflow problem you fix with better prompts. It is an architecture problem, and the fix is a skill file. QAJourney has a full breakdown of this system at qajourney.net/ai-qa-workflow-for-real-projects, including the actual skill files as free downloads. The short version: a skill file is a context document you load as a system prompt. It carries your test surface tiers, your three-path testing framework, your bug report format, your severity and priority logic, your Playwright conventions, and an explicit definition of what the AI does and does not get to call. Load it once per session. The AI operates inside your methodology from the first message instead of a blank slate. The local LLM layer solves a different problem. On a freelance or retainer engagement, tickets contain real product logic and real client data. Sending that to a cloud API on every session is a data exposure question whether or not it rises to a compliance issue. Running Ollama locally with the same skill file as system context keeps the engagement data on the machine. For the output quality required on QA tasks, current 7B to 14B models are sufficient. The cost at zero marginal per token makes it infrastructure rather than a service you pay by the session. The three-role setup in the workflow: engineer as judgment layer, cloud AI loaded with the skill file for complex reasoning and active session output, local LLM for lightweight tasks and client data work. The skill file is the constant across all three. The part that took time to internalize: AI dev teams already

RecursiveMAS (arXiv 2604.25917) showed that agents sharing internal reasoning state outperform agents that share only final outputs. The average accuracy gain across benchmarks was 8.3 points. The mechanism: each agent passes not just its answer but the latent embeddings from its own reasoning process, and the next agent conditions on both. The paper is a good result. The catch is access. RecursiveMAS requires open-weight models with hidden states exposed at inference time. That rules out Claude, GPT-4o, and Gemini. I built a Claude-native version using the Anthropic extended thinking API. The core idea transfers: instead of passing latent vectors, pass the full thinking text. The paper calls it internal state sharing; the Claude version calls it thinking-block relay. The architecture problem Claude's extended thinking blocks carry an encrypted signature tied to the originating conversation. You cannot pass a signed thinking block into a different agent's messages array. The API rejects it. The workaround: extract the text from the thinking block and inject it as a regular user message. # Extract thinking text from Agent 1 thinking_text = next ( ( b . thinking for b in response . content if b . type == " thinking " ), "" ) # Inject into Agent 2 as regular context, not as a thinking block context = f " Prior agent reasoning: \n { thinking_text } " The signature does not transfer. The reasoning does. relay-structured: what I built first The first architecture was a Planner > Critic > Solver loop where each agent emits a compact mental model JSON instead of raw thinking text. Raw thinking at a 1024-token budget is often compressed and fragmented. The hypothesis was that 150 tokens of structured signal carries more information per token than 1024 tokens of compressed prose. The schema each agent emits: { "interpretation" : "how the agent read the problem" , "key_steps" : [ "step 1" , "step 2" ], "rejected_approaches" : [ "approach tried and discarded" ], "confidence" :

I run Sola, a travel app for people who move differently from the traveller the industry was built for. While building it, I kept hitting the same wall. The data I wanted to query did not exist. Not because nobody collected it, but because the schema underneath the whole industry never had a field for it. So on 27 May 2026 I sat down and audited Booking.com. The homepage form, the currency selector, a Bangkok search results page. I wrote down what it accepts and what it refuses. Then I looked at the new AI travel agents shipping on top of it. Here is what I found, and why it matters to anyone building in this space right now. The form is the spec Booking.com's homepage search bar accepts exactly four inputs: A destination, as a single text field A check-in date and check-out date, as one range An occupancy counter, defaulting to "2 adults · 0 children · 1 room" A search button That is the spec. An online travel agency (OTA) is a CRUD app over this spec, and Expedia, Agoda, and Hotels.com run the same four fields. Airbnb lets you skip the dates. The destination stays a single field everywhere. Think about what a spec encodes. The default occupancy is a couple. Not a solo traveller, not a parent with one child, not three generations, not seven people eating from one host's kitchen. The form cannot accept a circuit ("Bangkok, then Hanoi, then Jakarta" forces three separate searches). It cannot accept an open date ("October, not sure which week"). It has no field for the part of a trip where you sleep at family but spend money in restaurants. When you fill that form, you have not searched. You have submitted to a schema. Most of the world's travellers fail the schema before they fail the search. The data receipts I am a builder, so I went for counts, not adjectives. Everything below rendered on the platform on 27 May 2026. Currencies: 52 offered, about 180 in circulation. Eight currencies sit featured at the top of the dropdown. On the day I ran it the order was EUR, US

A flanged pipe joint looks simple: two raised faces, a gasket between them, a ring of bolts pulling them together. Yet the gasketed bolted flange is one of the most common sources of leaks in process plants, and the reason is almost always the same — the bolts were not tightened to the right load. Too little and the joint weeps; too much and the gasket is crushed. The number that sits between those failures is the bolt preload, and it is not the same as the pressure load. This article explains how a bolted flange actually carries internal pressure, why the bolts must be preloaded well above the pressure end force, works a concrete example, and lists the mistakes that turn a sound joint into a leaking one. Why this calculation matters Bolted flange joints appear wherever a pipe or vessel has to be opened for maintenance: pump connections, valve bodies, heat exchanger shells, instrument tappings, and reactor manways. Unlike a welded joint, a flange is meant to be taken apart and reassembled, and every reassembly depends on the fitter applying the correct bolt load. The stakes are real. A leaking flange on a hazardous service can release flammable or toxic fluid. Even a benign leak wastes product and forces an unplanned shutdown. Design codes such as ASME Section VIII Appendix 2 set out a full method for sizing flange bolts, and at its heart is a comparison: the load the bolts can supply versus the load the joint demands in two distinct conditions — seating the gasket, and holding pressure. Understand the pressure end force and you understand the floor that the bolt load must clear. The core method When the line is pressurised, internal pressure acts on the fluid inside the flange and pushes the two flanges apart. The total separating force is the hydrostatic end force , the pressure acting over the area enclosed by the gasket sealing circle: H = p * (pi / 4) * G^2 Here p is the internal pressure and G is the gasket reaction (sealing) diameter — the effective circle on

⚠️ この記事はアフィリエイト広告（プロモーション）を含みます。リンク先で発生した収益の一部が運営者に支払われますが、読者の購入価格には一切影響ありません。 By the end of this article you'll have two runnable Python scripts: a CLIP-based pre-filter that re-checks SDXL Turbo output before it ever hits Pinterest, and a prompt sanitizer that strips artist names + trademarked characters so you don't eat a DMCA. I ran this pipeline for 41 days, generated 6,180 images, and went from a 9.7% Pinterest rejection rate down to 2.6%. Here's exactly what broke and what fixed it. Why SDXL Turbo (1-step, ~0.3s on a 4090) beats SD 1.5 for Pinterest volume First, the conclusion: if you're mass-producing pins, SDXL Turbo's single-step guidance_scale=0.0 generation is the only thing that makes the unit economics work. On my RTX 4090 I clock 0.31s per 512x512 image with Turbo vs 4.8s for a 30-step SDXL base run. That's 15x. Over 6,180 images that's the difference between 32 minutes and 8.2 hours of GPU time. But Turbo has a nasty side effect nobody warns you about: because it's distilled and runs at low resolution by default, its built-in StableDiffusionXLPipeline safety checker (when enabled) throws far more false positives on perfectly benign images — beaches, lingerie-free fashion flatlays, even close-up food. In my first 600-image batch, 58 images came back as black squares from the NSFW checker. 51 of them were photos of latte art and knitted sweaters . So I ripped out the default checker and built my own two-stage gate. Stage 1: Replacing the diffusers safety_checker with a tunable CLIP gate in Python The default safety_checker in diffusers is a binary black box — you get a black image and zero signal about why . For a production loop you need a confidence score so you can set your own threshold. I use OpenCLIP's ViT-B-32 to score each output against a small set of NSFW concept prompts, then compare to a safe-concept baseline. This code actually runs (tested on diffusers==0.27.2 , open_clip_torch==2.24.0 ): import torch import open_clip from PIL import Ima

System-prompt caching alone cut repeat-call costs by half Tool definitions cache separately, perfect for agent loops Conversation history caching pays off after turn three 1-hour TTL beats the default 5 minutes for batch jobs My Anthropic API bill dropped 70 percent last month and I did not change a single model. I changed where the cache breakpoints went. Here are the five patterns I now use on every Claude integration I ship. Pattern 1: Cache The System Prompt First The system prompt is the cheapest win and most people skip it. My agents run with a 4,000 token system prompt that explains the role, the output format, the safety rules, and a few examples. That prompt never changes inside a session. Before caching, I paid full input price for those 4,000 tokens on every single call. With an agent that loops 30 times to finish a task, that is 120,000 tokens of pure repetition. The fix is one parameter. I add a cache_control block with type: "ephemeral" to the last content item in the system prompt array. The first call writes the cache and costs slightly more (cache writes carry a small premium). Every call after that reads the cache at roughly one tenth the input price. Here is the rule I follow: the cached block has to be at least 1,024 tokens for Claude Sonnet, or it gets ignored silently. My 4,000 token prompt clears that easily. If your system prompt is short, this pattern does nothing, so do not bother adding the breakpoint to a 200 token instruction. The order matters more than people expect. The cache works as a prefix. Everything before the breakpoint gets stored. Everything after it is read fresh. So I put the stable stuff (role, rules, examples) up top and the volatile stuff (user query, current date) down below the breakpoint. Reorder this wrong and your cache hit rate collapses because the prefix changes on every call. One real number from my logs: a document-classification job that runs 2,000 times a day. The system prompt is 3,800 tokens. Caching it sav

Claude Code — My daily dev tool Claude Code by Anthropic is the one I use the most for development, by far. What sets it apart from the others: it integrates directly into the terminal and editor, it can read and modify files, navigate an entire codebase, and understand the global context of the project. Not just responding to a copy-pasted snippet in a chat window. In practice, when I have an idea, I ask it to structure the project and challenge my choices. And to be clear: I challenge it too. 😄 I sometimes disagree with its suggestions, and that's often where the conversation becomes interesting. It's a tool, not an oracle. Perplexity — My reference for research Perplexity is my main tool when I need a reliable and verifiable answer. It's a response engine that systematically cites its sources — you ask a question, it answers with excerpts from real web pages and direct links. No more hallucinations without references. However, I use it almost exclusively on desktop. On smartphone, it's flooded with messages pushing the paid version. Understandable from their side, but frankly annoying when you just want to do a quick search. 🙄 Gemini — For those in the Google ecosystem Gemini is Google's AI, and its main advantage is integration with Gmail, Docs, Drive, Sheets, and Google Search. I have a Google Pixel, and on that side, it does integrate very well with its own ecosystem. It's practical for analyzing documents or getting a quick summary without leaving the interface. That said, in terms of responses, it sometimes falters. 😬 Not systematically, but regularly enough that I stay on guard. And if privacy is a priority for you, it's worth thinking twice before entrusting it with your documents — I talk about this in my article on securing yourself on the Internet . ChatGPT — The natural entry point ChatGPT by OpenAI is the most known and most versatile AI. Writing, code, analysis, translation, summary, creativity... it does a bit of everything, often very well. The fre