Building AI-powered web apps with the MERN stack | Founder, Baloch Coder | BS Software Eng @UOPeshawar '25-'29

Peshawar, Pakistan
Joined August 2022
Interesting class of nodejs internals v8+cpp+libuv = nodejs coverd: Main thread and pool of threads Event loop threadpool IO polling setImmediate and many more Huge thanks to #Piyush sir, #jay isr, #Akash sir and #chaicode team.
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Just wrapped up the Chill Stream with Chai aur Vidyst, 1h 46m of pure chill and I still learned a ton! ☕🔥 Thanks for the great session @Hiteshdotcom @piyushgarg_dev @ChaiCodeHQ @nirudhuuu @yntpdotme @vidystdotcom 🙌
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Suppose you want to contribute in the field of technology to build a valuable product for Balochistan’s people, so what it will be?
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Before modern computers, there was Babbage. 💻 Charles Babbage designed the Analytical Engine, while Ada Lovelace wrote an early algorithm for it. #ComputerScience #TechHistory #Programming #AI #BalochCoder #asimalidev
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Sunburst showed that a trusted software update can become a major security risk. Supply-chain security, monitoring, and secure update verification are essential. #Cybersecurity #Sunburst #SolarWinds #InfoSec #CyberAwareness #BalochCoder
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Asim Ali | MERN & AI Developer retweeted
An 8-year-old built an app with AI to keep people and wildlife safe. His name is Viyoohan. Coyotes and bears kept turning up in his neighborhood. People didn't know how to react, and animals were getting hurt too. So he built Urban Wild Watch. You open a map, tap a button, report what you saw. Animal type, behavior, nearby landmark. There's a separate button for injured or orphaned animals that points you to the right people to call. It only shows the general area, not the exact spot, so nobody swarms the animal. How he built it: he used Claude to think through and organize the idea, then a tool called Lovable to turn plain English into the actual app. He edited it himself over a couple of days. It's already logged 150+ real sightings across Canada and the US. A few years ago this was a science-fair poster. Today it's a live app used in two countries, built by someone who hasn't finished grade school. The barrier to building isn't code anymore. It's whether you've got an idea worth building.
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He didn't know hahahahah
A vibe coder asked me: Why do we need a backend? Why not directly connect the client side app to the database? Reply: Why do you eat food and go to poop? Just throw the food directly in the toilet. Moral: There has to be some processing done in between.
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Asim Ali | MERN & AI Developer retweeted
We're open-sourcing Lily, Perplexity's local inference engine for serving models locally on Apple Silicon. This powers Perplexity's newly introduced hybrid compute feature for the Mac app.
Today we’re open-sourcing Lily, the local inference engine we built for hybrid compute in Perplexity Computer. Lily is specialized for Qwen3.6-35B-A3B on Apple silicon, built so on-device compute doesn’t bottleneck Computer tasks. Read more: perplexity.ai/hub/blog/optim…
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Asim Ali | MERN & AI Developer retweeted
underrated result of muse spark 1.3 try it out with muse code: curl -fsSL dev.meta.ai/install.sh | bash
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Asim Ali | MERN & AI Developer retweeted
Meta has released Muse Spark 1.3, their fourth Muse Spark model release in five months. Muse Spark 1.3 (max), which is in limited preview for Meta’s partners, scores 62 on the Artificial Analysis Intelligence Index, behind only Claude Fable 5.1 and Claude Opus 5. The variant available now, Muse Spark 1.3 (xhigh), scores 61 and ties with GPT-5.6 Sol (max) and Grok 4.6 (high). Both variants’ gains come primarily from improvements in agentic work and scientific capabilities Muse Spark 1.3 (xhigh) enters the Artificial Analysis Intelligence Index at 61, up 4 points from Muse Spark 1.2 (57, August) and 8 points from Muse Spark 1.1 (53, July). It enters tied with GPT-5.6 Sol (max), Grok 4.6 (high), and Claude Opus 5 (high), and behind Claude Fable 5.1 (max, 66), Claude Opus 5 (max, 63), and Claude Fable 5 (max, 62) Muse Spark 1.3 (max), which is in a limited preview stage, lands at 62. This higher index score is enabled by gains vs. Muse Spark 1.3 (xhigh) in Tau3-Bench Banking (52% vs. 47%) and GDPval-AA v2 (1,754 Elo vs. 1,709). Muse Spark 1.3 (max) is second only to Claude’s Fable and Opus variants in total score Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Continued improvement on agentic knowledge work tasks. At the launch of Muse Spark 1.2, we noted its significant gains in agentic knowledge work performance vs. Muse Spark 1.1. The latest iteration continues this trend, with Muse Spark 1.3 (xhigh) demonstrating a notable 12-point gain vs. Muse Spark 1.2 in Tau3-Bench Banking (35% to 47%), a 5-point gain in Terminal-Bench 2.1 (80% to 85%), and a new GDPval-AA v2 Elo of 1709 against its predecessor’s 1615. Muse Spark 1.3 (max) improves further on Tau3-Bench Banking (52%) and GDPval-AA v2 (1,754 Elo). This Tau3-Bench Banking score is #1 among all models. Muse Spark 1.3 (max) achieves these higher agentic work scores by using more turns and total reasoning tokens, reasoning 62% more on GDPval-AA v2 and 28% more on Tau3-Bench Banking compared to Muse Spark 1.3 (xhigh) ➤ The lowest cost per task for any model at 59+ on the Artificial Analysis Intelligence Index. Muse Spark 1.3 (xhigh) costs $0.55 per Intelligence Index task at Meta's unchanged $1.25/$4.25 per 1M token pricing ($0.15 for cached input), with its peers GPT-5.6 Sol (max) and Grok 4.6 (high) costing $0.95 and $0.94 respectively, a 70%+ premium. This places Muse Spark 1.3 (xhigh) on the Pareto frontier for Intelligence vs. Cost per Task. Its cost per task is higher than Muse Spark 1.2 ($0.40 per task), driven by ~57% more input tokens per task on agentic evaluations, with output tokens up only ~8%. Pricing for Muse Spark 1.3 (max) is not yet publicly available ➤ Scientific Reasoning results rose across the board, led by CritPt. CritPt was the standout non-agentic score gain vs. Muse Spark 1.2, with a material +8 points for the xhigh variant (18% to 26%), and GPQA Diamond achieved +4 points (90% to 94%), while Humanity’s Last Exam and SciCode each gained a more modest 2-3 points (45% to 47% and 56% to 59%, respectively). Muse Spark 1.3 (max) achieved roughly similar scores to the xhigh variant, gaining 2 points in Humanity’s Last Exam, tying on GPQA Diamond, and losing a point on CritPt vs. Muse Spark 1.3 (xhigh) ➤ Minor regressions in only two evaluations. Both Muse Spark 1.3 (xhigh) and Muse Spark 1.3 (max) dropped 4 points in AA-LCR (83% to 79%) when compared to Muse Spark 1.2, and AA-Omniscience (Accuracy) fell 3 points for xhigh and 1 point for max. The drops in AA-Omniscience (Accuracy) are due to a higher abstention rate (not answering questions when unsure), which also lowered the hallucination rate for Muse Spark 1.3 (xhigh) Other model details (xhigh variant): ➤ Context window: 1M tokens, unchanged from Muse Spark 1.2 ➤ Pricing: unchanged from Muse Spark 1.2: $1.25/$4.25 per 1M input/output tokens, with cache hits discounted to $0.15 per 1M ➤ Input modalities: text, image, video ➤ Availability: Meta's first-party API and Muse Code
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Asim Ali | MERN & AI Developer retweeted
Very excited to bring Muse Spark 1.3 to you! The whole team has been cooking to deliver major improvements in coding and agentic workflows 🏎️ Give it a try and let us know what you think!
1/ today we’re releasing muse spark 1.3—available in muse code & the meta model api. this is our most capable model yet—frontier performance almost too cheap to meter. much stronger at agentic and coding with better usability. we think users will really notice the jump.
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Asim Ali | MERN & AI Developer retweeted
honestly ngl meta might have cooked - cheaper than deepseek v4 flash $0.1 input $0.002 cached $0.2 output For non-sensitive stuff this is amazing
1/ today we’re releasing muse spark 1.3—available in muse code & the meta model api. this is our most capable model yet—frontier performance almost too cheap to meter. much stronger at agentic and coding with better usability. we think users will really notice the jump.
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Muse Spark 1.3 is rolling out... #musespark
Muse Spark 1.3 is rolling out today with frontier performance almost too cheap to meter. This is the biggest jump we've made so far on coding and agentic work. Try it in Muse Code and our API. Next up 🍉 and Muse Spark open weights releases coming soon.
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What's teh difference Generative AI and AGI? – at Karachi, Pakistan
🤖 Made with AI
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Asim Ali | MERN & AI Developer retweeted
Finally my first mac All thanks to @Hiteshdotcom
I think its time to change my window pc😭 subha sai on hi nhi horha Muje bhout zoro sai code aarha hai
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🤖 AI is becoming more useful for Baloch communities. From Balochi language tech & offline AI to smart farming, coding and remote work — the future of Balochistan is becoming digital. 🌐🚀 #AI #Balochistan #Balochi #BalochiAI #PakistanTech
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Asim Ali | MERN & AI Developer retweeted
Google just dropped Gemini 3.5 Transcribe and speech-to-text may never be the same. It understands context, removes filler words, handles self-corrections, recognizes custom vocabulary, and supports 85+ languages. With sub-second latency, it’s built for real-time transcription. Speech isn’t just input anymore. It’s becoming the interface.
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What if AI could actually work for Balochistan—even with limited internet? From learning and farming to Balochi language preservation, the next AI revolution could be closer than we think. Follow for more tech and ai updates. #AI #ArtificialIntelligence #Balochistan #Balochi
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🤖 Robots can now learn physical skills from just a few seconds of human demonstration — physical prompting is changing how machines learn. The bigger question: if robots can learn by watching us, what happens to human work? 👀 #AI #Robotics #PhysicalAI #ArtificialIntelligence
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