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Hello, I'm Shrijith Venkatramana, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. A language model can write Python, explain quantum mechanics, and imitate Shakespeare. Show it a screenshot of a production dashboard, however, and suddenly the central question becomes: How does a transformer that was trained on text learn what a pixel means? The naïve answer is: “Give the image to the LLM.” That description hides almost all of the interesting engineering. Modern multimodal systems are usually compositions of several models: a vision encoder turns pixels into vectors, a connector translates those vectors into something the language model understands, and the LLM then reasons over the resulting representation alongside ordinary text tokens. That architectural trick has turned the transformer from a language architecture into something much closer to a general-purpose interface for heterogeneous data. The evolution is worth understanding because it reveals a useful engineering pattern: you often do not need to retrain a giant model to give it a new sensory modality. You need a good representation and a sufficiently expressive interface between representations. 1. The basic mental model: pixels become tokens Start with an ordinary LLM. Its input looks conceptually like: "The server returned HTTP 500. What should I check?" | v tokenizer | v [t1, t2, t3, ..., tn] | v Transformer | v answer Everything is eventually represented as vectors. Multimodal transformers exploit this fact. An image is first converted into a sequence of vectors: image | v vision encoder | v [v1, v2, v3, ..., vm] | v multimodal connector | v [z1, z2, z3, ..., zk] | +------ text tokens [t1, t2, ...] | v LLM | v answer The important conceptual shift is this: The LLM does not have to understand pixels directly. It only has to understand

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AI can write code now. That part is no longer surprising. You can describe a feature to Copilot, Claude Code, Cursor, Codex, or another coding agent and get a working implementation in minutes. Sometimes it is genuinely impressive. But there is a bigger question: Can you actually trust the code enough to ship it? According to the Stack Overflow 2025 Developer Survey, 84% of developers use or plan to use AI tools . At the same time, trust in AI-generated output is still limited. One of the biggest frustrations developers report is getting an answer that is almost right, but not quite . Source: https://survey.stackoverflow.co/2025/ai And that “almost right” part is exactly where developers still matter. AI may write more code. But humans still need to decide whether that code is correct, secure, maintainable, and actually worth merging. So here is a simple review workflow I think every developer should practice. 1. Start With the Requirement, Not the Diff Imagine you tell an AI agent: Add password reset support. A few minutes later, it generates the full feature. The code may compile. The UI may work. The tests may even pass. But before reading the implementation, ask: How long should reset tokens remain valid? Can the same token be used twice? What happens if the email does not exist? Should existing sessions be logged out? Are we exposing whether a user account exists? This matters because AI can build the wrong thing very cleanly. So before asking: Does this code work? Ask: Does this solve the correct problem? That one question can save a lot of time. 2. Check the Architecture Before the Syntax AI is usually good at writing a function. It is not always good at understanding where that function belongs inside your system. For example, an agent might create something like: components/ ├── PaymentForm.tsx ├── PaymentAPI.ts ├── StripeService.ts └── Database.ts Everything may technically work. But should database access really live beside your UI components? Probably no

Why I built this Every time I applied for an internship, I did the same tedious thing: read the job description, guess what interview questions might come up, and google "common interview questions for [role]" — hoping something would stick. I wanted something smarter. Something that actually looked at my resume and the specific job description, and told me exactly where I stood and what to prepare. That's how PrepAI was born — an AI-powered career assistant that analyzes your resume against a job description and generates a match score, a skill-gap breakdown, personalized interview questions, and a 7-day preparation roadmap. 🔗 Live: prep-ai-navy-nine.vercel.app 💻 Code: github.com/Lalitprajapat47 What it does Upload your resume + paste a job description PrepAI extracts skills, experience, and keywords from both It generates: An ATS-style match score A skill-gap analysis (what the job wants vs. what you have) Personalized interview questions based on the actual role A 7-day roadmap to close the gaps before the interview Tech stack Frontend: React.js Backend: Node.js + Express.js Database: MongoDB AI: Google Gemini API for resume/JD analysis and question generation Classic MERN, with Gemini doing the heavy lifting on the reasoning side. The interesting part: prompting Gemini reliably The hardest part wasn't calling the API — it was getting consistent, structured output back every time. Interview prep needs predictable JSON (question lists, scores, roadmaps), not freeform paragraphs that break your UI. What helped: Being explicit in the prompt about the exact JSON shape I wanted back Feeding in resume text and JD text as clearly labeled sections, not just mashed together Adding a fallback parse step on the backend in case Gemini added extra text around the JSON This taught me a lot about prompt engineering as an actual engineering discipline — not just "ask nicely," but treating the prompt like an API contract. What I learned How to design a backend that talks to an LL

I have no idea what I'm f*cking doing. Something I figured out today: I do not start with the dark version of an idea. I start with a random curiosity, chase it because it is interesting, and then somewhere in the middle I look up and go: oh. This could turn bad. And it is probably already turning bad somewhere, run by someone who never bothered to look up. That happened again this week, while I was thinking about what I want my memory system to do next. So let me walk through the curiosity, and then the exact moment it flipped. AI memory is mostly boring Useful. But boring. Most memory systems store things like: what projects you are working on what tools you use what your preferences are what decisions you already made what facts should survive between sessions I built one of these. It is called mycelium. Connections between memories get stronger when I use them and fade when I do not, so it is a little more alive than a notes file. But at the end of the day it stores what I know. So an AI plugged into it eventually learns: I use Proxmox. I prefer LXC for a lot of workloads. I am building an operating system. I like local-first systems. I am suspicious of unnecessary dependencies. Cool. Accurate. Still not the thing I actually care about. It captures what I know. It does not capture how I think. And more specifically, it does not capture how I become curious. Humans randomly wonder about shit At least I do. I will be working on something unrelated and suddenly think: Wait, why does this work like that? Then: Has anyone tried it differently? Then: Is this whole abstraction actually necessary? And three hours later there is a new project directory on my machine and I am questioning all of my life choices. An LLM can generate questions if I ask it to. That is not the same thing. What it does not have is the persistent causal chain that led me, specifically, to ask certain kinds of questions over and over. A human brain does something like: event ↓ this feels weird ↓

1. Project Introduction and Objective In this project, I used Microsoft Excel and Power Query to clean and analyze a Jumia product dataset and then built an interactive dashboard to summarize pricing, discounts, ratings and customer engagement. The main objective was to turn a small raw e-commerce dataset into useful business information. I wanted the final dashboard to answer practical questions such as: Do products with higher discounts receive more customer engagement? Do higher priced products have better ratings? Is there a relationship between product rating and number of reviews? Which products have the highest review engagement? Which products may require further investigation because they have high discounts but low ratings? The project also gave me practical experience in data cleaning, excel formulas, PivotTables, PivotCharts, slicers, correlation analysis and dashboard design. 2. Dataset and Business Questions The original dataset contained 115 rows and 6 columns: Product Current price Old price Discount Review Rating The dataset was small but it contained several realistic data quality problems. This made it useful for me to practice the complete analytics process rather than going directly to visualization. I structured the workbook into the following sheets: Raw_Data Cleaned_Data Analysis Pivot_Tables Dashboard Data_Dictionary As we have always been taught in class,I kept the Raw_Data sheet unchanged so that I always have a copy of the original source data. 3. Initial Data-Quality Audit Before cleaning the data, I profiled the dataset in Power Query using Column Quality, Column Distribution and Column Profile. The audit identified several issues: Data-quality check Result Original rows 115 Original columns 6 Blank Review values 58 Blank Rating values 58 Populated Review values stored as negative numbers 57 Current Price ranges 1 Old Price ranges 1 Exact duplicate rows removed 3 Discount values outside 0 to 100% 0 Rating values outside 0 to 5 after cle

Agentic Methods: Coding With AI Agents, Designing For Agents TL;DR "Agentic methods" covers two distinct things colliding right now: AI agents that code alongside the team (read, write, run, verify, in a loop), and agentic architectures we design into our own systems (orchestrating autonomous agents on the product side). In both cases, the same principle applies: an agent is only useful if the contract around it is explicit — scope, errors, permissions, stopping points. The Tech Lead role doesn't disappear, it shifts: fewer lines typed, more specification, review, and governance. The underlying topic isn't tooling, it's clarity — exactly like a well-modelled business workflow. Table of Contents Introduction — one word, two meanings Coding with AI agents: what actually changes From autocomplete to the agentic loop The developer's role shifts toward review Explicit guardrails Designing agentic architectures An agent is a box with a contract Orchestration or autonomy: a choice, not a default Observability: if you can't replay it, you can't debug it Where humans remain irreplaceable A Tech Lead checklist for adopting these methods Conclusion — agents reveal a team's maturity Introduction — one word, two meanings "Agentic" has been everywhere for a few months, but it means two different things depending on who's talking: Coding with AI agents : a tool that reads code, writes diffs, runs commands, launches tests, and iterates until it reaches a correct result — instead of suggesting one line at a time. Designing agentic systems : a software architecture where autonomous agents (often themselves LLM-based) make decisions, call tools, and cooperate to accomplish a business task — a support chatbot that triggers refunds, a document pipeline that routes complex cases to a human on its own. These are two separate topics, but the same underlying principle runs through both: an agent — human, AI, or service — is only reliable when it operates inside an explicit frame. It's the s

A familiar production failure looks like this: an AI support agent reads a ticket, decides the customer deserves compensation, calls the refund tool, and refunds the full annual subscription instead of the $12 add-on. The model did not crash. The API did not throw an exception. The tool worked exactly as designed. The postmortem usually starts with the wrong question: “How do we stop the model from making bad decisions?” The better question is: which layer should have stopped the mistake before it became damage? AI agents fail in many different ways. They misunderstand intent. They create dangerous plans. They pass malformed arguments. They exceed permissions. They loop. They leak data. They take irreversible actions. Each failure mode belongs to a different layer, and each layer has a different job. If your only defense is a prompt that says, “Be careful,” you do not have a safety architecture. You have a hope. TL;DR: AI agent mistakes should not be stopped by the model alone. Use layered defense: intent classification stops wrong missions, plan validation stops forbidden sequences, tool schemas stop invalid arguments, authorization stops unauthorized actions, execution controls limit blast radius, output validation catches harmful results, runtime monitors stop loops, and human approval guards asymmetric risk. The best stopping layer is the earliest deterministic layer that can prevent harm, with the final brake closest to irreversible side effects. 📋 Table of Contents The Mistake Is Not One Failure Mode 1. The Prompt Layer Should Persuade, Not Enforce 2. The Intent Layer Should Catch the Wrong Mission 3. The Planning Layer Should Reject Forbidden Paths 4. The Tool Contract Layer Should Make Invalid Actions Unrepresentable 5. The Authorization Layer Should Veto Even Correct-Looking Actions 6. The Execution Layer Should Make Side Effects Boring 7. The Output Layer Should Catch Harmful Results Before They Ship 8. The Runtime Monitor Should Stop Slow-Motion Failures

A simple browser-local PNG-to-PDF workflow For this kind of job, the useful workflow is straightforward: Select the PNG, JPG, or JPEG files. Put the pages in the order they should appear. Choose a page size and margins if the document needs them. Export one PDF. The important detail is where the conversion happens. A browser-local PNG-to-PDF tool processes the images in the browser instead of uploading them to a conversion server. That makes it easier to keep control of source files while still producing one shareable PDF. When this is useful This workflow is handy for: combining screenshots into a bug report or handoff document; turning scanned pages into one file for email or printing; arranging portfolio images or design exports in a deliberate order; and collecting receipts or reference images without making a separate document first. Before exporting, check the page order and decide whether each page should match the image, A4, or US Letter. A preview is useful here: it catches a stray portrait page, an oversized margin, or a screenshot in the wrong position before the PDF is created. The tool I use for this I maintain PNG Binder , a free PNG-to-PDF converter for this specific workflow. It accepts up to 50 PNG, JPG, or JPEG images, lets you arrange them, and creates one PDF locally in the browser. It does not require an account, and the images are not sent to a conversion server. It creates an image-based PDF, so it does not perform OCR or rebuild text and tables. If that is the kind of result you need, try it and let me know whether page ordering, page settings, or browser compatibility could be improved. Disclosure: I am the maker and operator of PNG Binder.

Apple is expected to announce several products at its September event next week—including a folding phone—but the iPhone 18 might not be among them. It would be a first for the company.

I've built enough animated portfolio sites and agency landing pages at this point that I can usually tell within the first three seconds of scrolling whether a site was built by someone who actually understands scroll animation, or someone who just copied a GSAP tutorial and called it a day. And honestly, for a long time, I was the second guy. I remember the first time I tried to recreate one of those Awwwards style hero sections, the ones where text fades and slides as you scroll and everything feels buttery and expensive. I copied the GSAP code almost exactly from a tutorial. Same triggers, same easing, same everything. On my laptop, using my trackpad, it looked incredible. I was proud of it. Then I opened it on my client's Windows machine with a regular mouse, and it looked like it was having a seizure. Stuttering, jumping, completely different animation than what I built. That was the moment I realized the problem was never really the animation. The problem was what the animation was reading from. That thing is scroll. And native browser scroll is honestly kind of a mess. Why native scroll ruins your animations Here's the part nobody explains properly when they show you a GSAP demo. When you scroll a normal webpage, the browser doesn't give you a smooth continuous stream of scroll position. It gives you scroll position in little discrete jumps. How big those jumps are depends on the device, the input method, the browser, even the operating system. A trackpad on a Mac behaves differently than a mouse wheel on Windows, which behaves differently again on a touchscreen. Now think about what ScrollTrigger is actually doing under the hood. It's constantly reading your scroll position and mapping it to animation progress. If the scroll position itself is jumpy and inconsistent, then no matter how well you write your animation code, the output is going to inherit that same jumpiness. You could have the most perfectly tuned easing curve in the world and it still won't ma

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Ever since I got involved with local LLMs I wanted to share the magic with my friends. The process before involved either Ollama or llama.cpp, which are great, but the setup was difficult and a barrier to entry for most people. WHAT ARE THE BENEFITS OF LOCAL AI? Local AI isn't as powerful as cloud-based solutions, but the gap is narrowing. With local AI there are no subscription costs, token limits, or outages, since it all runs on your own hardware. It doesn't require an internet connection, so it can be used fully offline. For businesses that are worried about leaking IP or sensitive data it's especially attractive. It stays on your machine and your data doesn't get captured by some company that may or may not use it to train their next model. WHAT YOU NEED FIRST Before we get started you need to understand what your hardware is capable of. For this to work well I suggest an Apple Silicon Mac with at least 24 GB of unified memory, or a gaming desktop with at least 16 GB of VRAM. The more VRAM you have, the more capable models you will be able to run. For reference, I run it on three machines: a MacBook Pro with 96 GB of unified memory, a Mac Mini with 24 GB, and a gaming desktop with a Radeon 7900 XTX. ONE INSTALLER, NO SETUP Unsloth Desktop is what people have been waiting for. It's just been released as a beta. It's pretty much a single-click install. You download the installer and run it, and from there Unsloth Desktop handles everything else for you. Behind the scenes it scans your machine and determines what needs to be installed. It puts a wrapper around llama.cpp and MLX, which gives you all the power of the top open source models without having to manage the underlying tools. Unsloth Desktop will automatically detect if any of the tools have gotten any updates and will prompt you to install the updates. MODELS COME STRAIGHT FROM HUGGING FACE Not only does Unsloth Desktop make the initial install easy, it integrates directly with Hugging Face. For those who

The number that surprised us We build software for two businesses that sound like the same business. One rents things out by the day. The other sells and manages property . Described in a sentence, both are someone paying to use a building or a vehicle for some period of time. When we started the second one, everyone involved assumed most of the first would carry over. Between them, the two products describe 113 business concepts — the things the software has to know about, like a customer, a contract, a price rule, a booking. Seven are shared. Six percent. The second product still shipped far faster than the first. Understanding why is worth more than the number itself, because the same logic decides whether an automation project inside your own company pays for itself. Why two similar businesses share almost nothing The sentence that makes them sound alike is the sentence hiding all the differences. A rental company has vehicles. They exist or they do not. A property developer has buildings under construction, where each apartment moves through stages — planned, framed, finished, ready to hand over — and half the business is tracking which stage each one is in. There is no version of a car that is sixty percent delivered, so there was nothing in the first product to borrow. A rental booking opens and closes inside a week. A property sale runs for months and involves a buyer, a seller, an agent, and often a bank, each of whom needs their own view of the same transaction. We know exactly how far you get by treating that as a booking with extra fields: right up until the first commission has to be split three ways. And a rental company has customers. A property company has customers, owners and investors — people who never buy anything through the system and log in only to see what their asset is doing. There is no equivalent at all in the first product, which is the clearest sign that these were never the same business. Where the savings actually were Nothing above