GitHub - nikhilt101/email-blast-tool: Open source HTML email sender tool using CSV/XLSX + Gmail SMTP · GitHub Open source HTML email sender tool using CSV/XLSX + Gmail SMTP - nikhilt101/email-blast-tool github.com

Now that almost everyone has thought about or is actively integrating AI workflows into their projects, some might ask is this all worth the cost? Many think the current economics of the AI space don't scale and that there will be upward price movement. Others still might not be comfortable with sending their data to remote services for processing. Then there is the crowd that wants to deploy models in small spaces with limited compute. Are there ways we can deploy small models locally and run at a lower cost? Yes with Knowledge Distillation . Knowledge distillation can get a bad rap due to it's questionable use in training some Large Language Models (LLMs). But it's a perfectly valid way to transfer performance from a larger model to a smaller one. Especially when both models are yours and/or open. This article will explore progressive distillation which is a technique to incrementally transfer knowledge from a series of larger teacher models into a smaller student. Install dependencies Install txtai and all dependencies. pip install txtai [ pipeline - train ] datasets Setup the Training Pipeline The first step we need to do is setup up the training pipeline. We'll use the Hugging Face Training framework to build a series of models. The following code establishes a train method, test method and loads the classification training data. from datasets import load_dataset from transformers import AutoModelForSequenceClassification , AutoTokenizer from txtai.pipeline import HFTrainer , Labels def train ( teacher , student , distillation , ** kwargs ): trainer = HFTrainer () model = AutoModelForSequenceClassification . from_pretrained ( student , trust_remote_code = True ) tokenizer = AutoTokenizer . from_pretrained ( student , trust_remote_code = True ) return trainer ( ( model , tokenizer ), ds [ " train " ], columns = ( " sentence " , " label " ), maxlength = maxlength , teacher = teacher , distillation = distillation , ** kwargs ) def test ( model ): labels = Labels (

The Theorem That Changed How We Think About Databases In 2000, Eric Brewer stood at a conference and proposed a conjecture that would reshape distributed systems forever: "You can only guarantee two of these three properties at the same time: Consistency, Availability, and Partition Tolerance." Two years later, Seth Gilbert and Nancy Lynch proved it mathematically. It became known as the CAP Theorem — and every distributed system architect since has had to wrestle with it. It sounds abstract. But once you understand it, you'll never look at a database choice the same way again. You'll understand why Amazon DynamoDB and Google Spanner make opposite architectural choices. You'll know why your bank uses PostgreSQL while Twitter uses Cassandra. Let's break it down from first principles. The Three Properties C — Consistency Every read receives the most recent write, or an error. There's only one version of the truth — all nodes agree. Not the same consistency as ACID . CAP consistency (linearizability) means every read reflects the latest write across all nodes. ACID consistency means transactions don't violate database constraints. Different concepts, same confusing word. A — Availability Every request receives a non-error response — though it might not be the most recent data. The system is always up and answering. Note: "Available" in CAP doesn't mean "fast." It means "responds without error." A system that always returns a (possibly stale) answer is Available. P — Partition Tolerance The system continues operating even when network messages between nodes are lost or delayed. A partition is when part of your distributed system can't communicate with another part. The Unavoidable Truth: P Is Not Optional Here's the insight that makes CAP actually useful: In any real distributed system, partitions will happen. Networks fail. Cables get cut. Data centers lose connectivity. AWS regions go down. Since you must tolerate partitions (or have a single-server system, which does

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You build a tournament bracket with a popular React library. In Chrome it's perfect — neat columns, clean connector lines. Then you open it on an iPhone, or in Safari, or inside your Capacitor app… and every match is crammed into the top-left corner, stacked on top of the round headers. If you've ever shipped a bracket to iOS, you've probably seen this exact bug. Here's why it happens — and a tiny library that fixes it for good. The symptom It looks fine everywhere Chromium runs (Chrome, Edge, Android WebView) and completely broken everywhere WebKit runs: Safari (macOS and iOS) iOS WKWebView Capacitor / Cordova apps Electron-on-WebKit The matches don't just shift a little — they all render at coordinate (0,0) of the bracket, piling on top of each other and the headers. The cause: SVG <foreignObject> in WebKit Most React bracket libraries — @g-loot/react-tournament-brackets , react-tournament-bracket , and friends — render the bracket as an SVG and place each match's HTML inside a <foreignObject> positioned with x / y attributes. WebKit has a long-standing bug: it ignores x , y , and transform on <foreignObject> and positions the content relative to the top-level <svg> instead of the foreignObject's own coordinates. Every match therefore collapses to the origin. And there's no CSS escape hatch — x , y , and transform are all ignored on foreignObject in Safari, so you can't nudge the content back into place. I even tried patching a library to wrap each match in a <g transform="translate(x,y)"> instead of a nested <svg x y> ; WebKit ignores ancestor transforms for foreignObject positioning too. The SVG approach is simply a dead end on WebKit. The fix: don't use SVG at all A bracket is really just columns of cards joined by connector lines — and both are expressible in plain CSS. Here's the key insight. Put each round in a flex column where every match sits in an equal flex: 1 slot. Because each round has half the matches of the previous one, a match's slot spans exactl

By Suryansh Sinha (sinxcos07) Introduction Recently, I built AstroFit , a fitness-focused web application as a personal project to learn more about modern web development, deployment, databases, and building complete applications from idea to production. This project helped me understand how different parts of a web application work together, from the user interface to backend functionality and deployment. Why I Built AstroFit I wanted to work on a project that felt practical and useful while also helping me improve my development skills. Instead of creating a simple clone project, I decided to build a fitness application where I could experiment with real-world features and deployment workflows. Development Journey Building AstroFit involved much more than just creating pages and connecting them together. Some of the areas I explored while working on this project included: Frontend development Backend integration Database management Authentication systems Deployment and hosting Debugging production issues One of the biggest learning experiences was understanding how different technologies communicate with each other in a complete application. Future Plans I plan to continue improving AstroFit by adding more features, refining the user experience, and expanding its capabilities over time. This project is still evolving, and I'm excited to keep working on it. Project Links Live Demo: astrofit-fitness.vercel.app GitHub: sinxcos07 / astrofit-frontend Fitness platform combining workout tracking and astrology-inspired personalization. AstroFit AstroFit is a modern fitness web application that combines workout tracking with astrology-inspired personalization to create a unique and engaging fitness experience. Features Modern responsive UI Astrology-inspired fitness experience Workout tracking interface User authentication system Backend integration Smooth and interactive design Mobile-friendly layout Tech Stack Frontend HTML5 CSS3 JavaScript Backend Node.js Express.js SQL

I have been using it heavily for about a year and lately I notice I can almost feel when something was written by it. There is a certain rhythm to it, the way it structures paragraphs, the way it wraps up with a summary sentence, the way transitions feel slightly too smooth. It is hard to explain but once you see it you cannot unsee it. What I find interesting is that even after editing ChatGPT output pretty heavily those patterns seem to stick around at a sentence level. The words change but something underneath stays the same. I started verifying this by running edited drafts through a few different tools and the results were eye opening. Some tools completely missed the patterns, others picked them up even after significant rewrites. Makes me wonder how much of what we read online right now has that same fingerprint sitting underneath it and we just do not realize it yet. Has anyone else started noticing this or developed a sense for spotting it just from reading? submitted by /u/Few-Education7746 [link] [留言]

A WordPress plugin update breaks the site. You SSH in to roll back the bad plugin with WP-CLI, and you get this: Fatal error : Uncaught Error : ... in / path / to / broken - plugin / main . php : 42 The plugin you came to fix has now stopped the tool you came to fix it with. It looks contradictory, but it makes sense once you know how WP-CLI starts up — and there's a flag pair that gets you out. Why WP-CLI itself crashes When you run a rollback command like wp plugin install <name> --version=X --force , WP-CLI internally boots WordPress before doing anything else . Plugin registration and option loading all happen during WordPress's startup, so a broken plugin gets loaded there, throws a fatal, and takes the WP-CLI process down with it. The sequence: WP-CLI boots WordPress WordPress loads the active plugins The broken plugin throws a fatal WP-CLI exits before ever reaching the file-replace step The actual rollback (downloading the PHAR, overwriting the plugin directory) never gets a chance to run. The fix — safe-mode flags WP-CLI has two startup flags, --skip-plugins and --skip-themes . With both set, WordPress's startup skips loading any plugins and themes at all . wp plugin install <name> --version = X --force --skip-plugins --skip-themes File-system operations (downloading the PHAR, unpacking it, replacing files) don't depend on plugin code, so they run fine. The broken plugin never gets loaded at boot, so it never fatals, and the rollback completes. Should you set these flags everywhere? You might think "why not just add these to every WP-CLI command by default?" But some commands genuinely need plugins or themes loaded. wp cache flush relies on the object-cache plugin's hooks. wp doctor reads diagnostic information that plugins register. Setting safe-mode flags on those would break them in subtle ways. The practical split: file-operation commands always get --skip-plugins --skip-themes . Cache and diagnostic commands don't. That single rule eliminates the worst

"A language model that answers questions is a tool. A language model that decides which questions to ask and then acts on the answers is something else entirely." Introduction: When Models Started Deciding For the first several years of modern NLP, the task was always the same: given input, produce output. One forward pass. One completion. Done. In 2022, a paper from Google Brain asked a different question. What if, instead of producing an answer directly, a model could reason about what information it needs, act to retrieve it, and revise its thinking based on what it found? The paper was ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022). Applying it to an LLM created something qualitatively different: a model that could take real-world actions and adapt its reasoning based on what came back. A completion model is a calculator. An agent is a process: it has a goal, takes steps toward it, and updates when things go wrong. This week I went deep on the architecture behind these systems, the frameworks that define them, and what the open problems look like from a research perspective. Part 1: What Makes a System "Agentic"? The word "agent" gets used loosely in current literature. A clean definition comes from Russell and Norvig's Artificial Intelligence: A Modern Approach : An agent is anything that perceives its environment through sensors and acts upon that environment through actuators. For an LLM-based system, this is a loop: perceive an observation, reason about what to do, act via a tool call or output, observe the result, and loop again. But not every loop qualifies as agentic. Three properties distinguish genuinely agentic systems from tool-augmented chatbots: Property What It Means Goal persistence Maintains the original goal across multiple steps without re-prompting Adaptive planning Revises its approach based on intermediate results Tool autonomy Decides when and which tools to use, not just how to use one it was told to call Mos

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent What if the biggest limitation in AI today isn't reasoning, model size, or context windows? What if it's memory? Every morning, millions of people open ChatGPT, Claude, Gemini, or another AI assistant and start a conversation. The AI seems intelligent. It writes code. It explains concepts. It helps brainstorm ideas. It can even help design an entire software architecture. Then the conversation ends. Tomorrow? It remembers nothing. Imagine hiring a senior engineer who forgets everything at the end of every workday. Every morning you would need to explain: What your company does How your product works Which technologies you use Why certain decisions were made What happened yesterday Nobody would call that employee productive. Yet this is exactly how most AI systems operate. And it reveals something important: Most AI agents aren't actually learning from experience. They're simply reasoning over whatever context happens to be available right now. That distinction may define the future of agentic AI. Because the next generation of AI won't just need better reasoning. It will need memory. And that's where Hermes Agent becomes interesting. The Strange Reality of Modern AI The public perception of AI often looks like this: User → AI → Intelligence But the reality is closer to this: User → Context Window → AI → Response The AI only knows what exists inside its current context. Once that context disappears, so does most of its understanding. This is why many AI experiences feel surprisingly repetitive. You spend 30 minutes explaining your project. The AI finally understands your goals. The answers become better. The recommendations become more relevant. Then the session ends. The next conversation starts from scratch. Not because the model isn't powerful. But because the knowledge never became persistent. Context Windows Are Not Memory A context window is not memory. It is temporary working space.

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]