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The actor, who starred in 1993's Jurassic Park , died Monday in Sydney, Australia, at the age of 78.

Freelancing in software development sounds like the ultimate dream: Work from anywhere 🌴 Choose your own projects 💻 Set your own rates 💰 But for developers in India, the reality often looks like this: A client requests "just one small change" for the 15th time. You deliver the code, and the client suddenly goes ghost. You get your payment but realize 10% was deducted, and you have no idea how to file your taxes. You realize you are charging ₹500/hour but spending half your time on unpaid admin work. I've been there. Most Indian developers start freelancing with great coding skills but zero business systems. Here is a step-by-step guide on how to protect yourself, price your work correctly, and handle Indian taxes (GST/TDS) like a professional. Stop Doing Unpaid Requirements Gathering (Use a Questionnaire) When a client says: "I want an e-commerce website, how much will it cost?" If you jump on a call and spend 2 hours discussing it without any commitment, you are losing money. The Fix: Before scheduling a call, send them a structured Project Intake Questionnaire. Ask about: Business model and target audience. Tech stack preferences & required integrations (Stripe, Twilio, WhatsApp API). Brand assets and Figma designs (Do they have them, or do you need to design too?). Budget range & expected timeline. If a client refuses to fill out a 10-minute form, they aren't serious about hiring you. Eliminate "Scope Creep" with a Scope Document "Scope Creep" is when a client keeps adding features without paying extra. It kills developer profit margins. The Fix: Always draft a Project Scope Document before signing a contract. It must have two clear sections: Included in Scope: Detailed feature list (e.g., "User login via Google, 3 database models, responsive UI"). EXPLICITLY OUT OF SCOPE: Things you will NOT do (e.g., "Logo design, copywriting, free server maintenance post-launch"). Include a Change Request (CR) clause: "Any feature requested outside Section 1 will be billed at

DPDP compliance costs for Indian startups: what to budget before 13 May 2027 Summary. Full compliance with India's Digital Personal Data Protection Act is due on 13 May 2027. That is the date consent, notice, security safeguards, breach intimation and data-principal rights all become enforceable, and it is roughly 10 months away. The DPDP Rules 2025 were notified on 13 November 2025 and land in three phases: the Data Protection Board of India stood up immediately, penalties and Consent Manager registration begin on 13 November 2026, and everything else bites on 13 May 2027. The penalty schedule is not proportionate to your size: up to ₹250 crore for failing reasonable security safeguards, ₹200 crore for failing to notify a breach, and ₹150 crore for missing Significant Data Fiduciary obligations. There is no revenue or headcount exemption. The cost gap is where founders get hurt. Vendors routinely quote ₹15 lakh to ₹2 crore for DPDPA compliance, while a startup under 10,000 users can be substantively compliant for under ₹50,000 a year. India's privacy and data governance market is worth roughly $1 billion to $1.5 billion today, per IDfy founder Ashok Hariharan speaking to Inc42 in April 2026, and a lot of that revenue depends on you not reading the rules yourself. This is a budgeting guide, not a legal opinion. The rules are short. Read them, then price the work. The deadline that actually matters There are three dates, and only one of them is a real deadline for most startups. Phase Date What switches on Does it affect a typical startup? Phase 1 13 November 2025 Data Protection Board of India established, Rules notified No direct obligation, but the Board can already receive complaints Phase 2 13 November 2026 Consent Manager registration opens, penalty framework and enforcement powers begin Only if you intend to register as a Consent Manager Phase 3 13 May 2027 Notice, consent, security safeguards, breach intimation, retention, children's data, data-principal righ

Apple announced the new CarPlay feature not long after Google announced a similar update for Android Auto.

Babies are tremendous learning machines, and key advances for AI may soon be found in the architecture of their little brains.

Inkling, a 975-billion-parameter open source model, was trained to understand video and audio. It could help Thinking Machines establish itself among competitors like Anthropic and OpenAI.

OnePlus could also wind down its operations in India by 2027.

Google's biggest solar and battery project stands in sharp contrast with xAI's nearby unpermitted power plant.

Lucid Motors found itself in a tough bind this week, fending off bankruptcy rumors and watching its stock price plunge as a result. The company quickly denied the report, calling it "completely false" and pointing to its available free cash flow as evidence that it has enough runway to operate into next year. But despite […]

OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet. GPT-Red automates…

Don't use Amazon Music? Too bad, Samsung is installing it for you anyway.

Livestream shopping platform Whatnot has acquired AI startup Shaped, a machine learning company focused on real-time recommendations and search. The deal will bolster Whatnot’s personalization and discovery features as it expands into new product categories.

Ever sat down and thought about how a movie can spark your curiosity about technology? I was rewatching "Jurassic Park" recently, and, for the umpteenth time, I found myself mesmerized not just by the dinosaurs but by the computers! The way they portrayed tech in the early '90s was a mix of excitement and pure whimsy. I’ve been exploring the tech behind the magic, and it’s been a wild ride down memory lane—a nostalgia trip mixed with some surprising insights into how things have evolved. A Walk Down Memory Lane When I first watched "Jurassic Park" as a kid, the scene where Dr. Ellie Sattler runs through the control room, frantically trying to restore the park’s security, left me awestruck. I mean, who didn’t dream of typing on one of those cool-looking computers? As a budding developer, I couldn't help but wonder about the behind-the-scenes tech. Ever wondered why they used UNIX systems? Or why the computer graphics felt so cutting-edge back then? Turns out, they were leveraging a blend of SGI workstations and proprietary software that made their visual effects legendary. I remember my first experience with UNIX during my college days, and it felt like being dropped into a different universe—powerful, complex, and sometimes, downright intimidating. I’ve learned that just like in the movie, the power of tech lies in how effectively we can wield it. The Nostalgia of User Interfaces Let’s talk about user interfaces. The interfaces portrayed in the film, with their vibrant colors and flashy animations, were quite ahead of their time. It’s funny looking back because, at points, they seemed so unrealistic. I mean, the way Dr. Ian Malcolm effortlessly navigated the systems? I wish it was that easy! When I started working on UI/UX projects, I learned that simplicity is key. I once spent hours creating a beautiful interface that was so complex no one could figure it out! My takeaway? Sometimes, less is more. It’s the same lesson I’ve carried into modern frameworks like React

The Codex Micro is designed to monitor multiple agentic threads at a glance.

Risk stratification sounds like a data-science buzzword until you have to build the thing. For a Medicare Advantage plan, it's a concrete pipeline: take a population of members, score each one's clinical and financial risk, and rank them so care management and documentation teams know who to touch first. Here's how I'd architect it. The core idea Population health risk stratification = scoring + segmentation. You compute a per-member risk signal, then bucket members into tiers (e.g., rising-risk, high-risk, catastrophic) so finite resources go where they move outcomes and revenue most. The mistake teams make is treating it as a single ML model. In practice you want a layered signal: a stable, explainable base (RAF + chronic conditions) plus optional predictive overlays. Explainability matters because care managers won't act on a black-box score, and auditors won't accept one. Step 1: Build the member feature record { "member_id" : "SYNTH-77310" , "age" : 73 , "hccs" : [ "HCC37_1" , "HCC85" , "HCC18" ], "raf" : 1.842 , "gaps" : [ "a1c_overdue" , "no_pcp_visit_180d" ], "utilization" : { "ed_visits_12m" : 3 , "inpatient_12m" : 1 } } The RAF here is your defensible, model-grounded risk anchor under CMS-HCC V28. Everything else is supplemental signal. Step 2: Score and tier def risk_tier ( member ): base = member [ " raf " ] util = 0.15 * member [ " utilization " ][ " ed_visits_12m " ] \ + 0.30 * member [ " utilization " ][ " inpatient_12m " ] score = base + util if score >= 3.0 : return " catastrophic " if score >= 1.8 : return " high " if score >= 1.0 : return " rising " return " stable " Keep the weights transparent and tunable. The point isn't a perfect model; it's a defensible, reproducible ranking your operational teams trust. Step 3: Make "rising-risk" actionable The tier that quietly drives the most ROI is rising-risk — members trending toward high cost who still have open documentation and care gaps. Surface their specific gaps (overdue labs, undocumented chroni

The Quest Begins (The “Why”) I still remember the night our API started to sputter under a sudden traffic spike. Users were seeing 502 errors, the monitoring dashboard looked like a neon rainstorm, and I felt like I was stuck in a lobby waiting for the elevator that never arrives. We had a simple round‑robin load balancer sitting in front of three identical services. It worked fine when traffic was smooth, but as soon as a burst hit, one node would get overloaded while the others twiddled their thumbs. Honestly, I thought we just needed more servers. Throwing hardware at the problem felt like using a sledgehammer to crack a nut—expensive and messy. After a few frantic Slack threads and a lot of coffee, I realized the real issue wasn’t capacity; it was how we distributed the work. The balancer was oblivious to the actual load on each backend, treating every request like it was the same weight. That moment became my quest: design a load balancer that reacts to real‑time load, stays simple enough to operate, and doesn’t cause a reshuffling nightmare when we scale the cluster. The Revelation (The Insight) The breakthrough came when I read about least‑connections load balancing combined with a slow‑start period for new hosts. The core insight is deceptively simple: Send each new request to the backend that currently has the fewest active connections. Why does that work? Immediate fairness – If one node is handling long‑running requests, it will naturally have a higher connection count and receive fewer new ones until it catches up. Burst absorption – During a traffic spike, requests spill over to the less‑busy nodes instead of piling onto a single overloaded instance. Predictable scaling – When we add a new server, it starts with zero connections, so it gets a fair share of traffic right away—but we temper that with a slow‑start window to avoid overwhelming a cold host. Compare that to round‑robin, which blindly cycles through the list regardless of each node’s state. In

You've got a recording and you want text back. For years that meant one thing at AssemblyAI: submit the file, wait for the job to finish, get a transcript. Async. It's reliable, it's cheap, and for a huge range of workloads it's exactly right. But "wait for the job to finish" is doing a lot of work in that sentence. If your file is two minutes long and your user is staring at a spinner, waiting is the whole problem. That's the gap the Sync API fills — and it's why "which transcription path" is no longer a two-way question. This post is about the two ways to transcribe a recording : async and sync. (If you're deciding between recorded and live audio in the first place — streaming versus the rest — start with our guide to real-time vs batch transcription , then come back here to choose between the two non-streaming paths.) The one-sentence difference Async transcription hands you a job: you submit audio, the work happens in the background, and you collect the result later by polling or via a webhook. Sync transcription hands you an answer: you POST a short clip and the transcript comes back in the same HTTP response — no job to track, no callback to wait for. Everything else follows from that. Async is built for throughput and depth on files of any length. Sync is built for speed on short files, when a person or an agent is waiting on the other end. How fast can each actually go? This is the question that usually settles it, so let's be concrete. Async processes the whole file and returns a single complete transcript, typically in seconds to a few minutes depending on file length and load. Crucially, it bills on audio duration ($0.21/hr on Universal-3.5 Pro), so a 30-minute file costs the same whether it comes back in 20 seconds or two minutes. You're optimizing for cost and completeness, not for the clock. Sync is built to return a transcript for a short clip almost immediately — roughly 134ms p50 — in one request/response, with no polling and no webhooks. It's price

Can a handful of engineers really do the work of an army of consultants? That’s the bet behind Ode with Anthropic — the joint venture dedicated to embedding forward-deployed engineers in enterprise firms, backed by Anthropic, Blackstone, Hellman & Friedman, Goldman Sachs and others. On this episode of TechCrunch’s Equity podcast, Rebecca Bellan sits down with Ode’s leaders Chris Taylor and Eddie Siegel, who founded Fractional AI, […]

New Delhi announced a $6.5 billion smartphone manufacturing program and a $13.3 billion semiconductor push to deepen India's electronics supply chain.

Similar to last time, What’s !important #15 is pretty stacked — read all about boundary-aware CSS, making grid lanes accessible, creating time-based web designs, fixing full-bleed CSS, improving customizable select, new web platform features, and more. What’s !important #15: Boundary-aware CSS, Time-based CSS, Full-bleed CSS, and More originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.