Abhinav Prakash (@NeurlAP) header
Abhinav Prakash (@NeurlAP) profile picture

Senior Applied Gen AI Engineer @PocketFM_App · prev Backend @mydukaanapp

Bangalore
Joined January 2018
Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)Photo by Abhinav Prakash (@NeurlAP)
Abhinav Prakash retweeted
What's impressive is that agents run within your own infrastructure, using your own data and models. Combining trace-level debugging with plain-English fixes while keeping full control of execution is a brilliant approach. 👏
1/5 Domain experts can’t touch the code. Engineers can’t fix what they can’t judge. So we built Agent Doctor. A lawyer, banker or doctor opens a run, finds the broken step, and fixes it in plain English. No agent code changes. No redeploys. Your agent stays on your servers. Two lines to start.
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Abhinav Prakash retweeted
This is super cool! Video made with @genmotionhq 🫶
1/5 Domain experts can’t touch the code. Engineers can’t fix what they can’t judge. So we built Agent Doctor. A lawyer, banker or doctor opens a run, finds the broken step, and fixes it in plain English. No agent code changes. No redeploys. Your agent stays on your servers. Two lines to start.
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Abhinav Prakash retweeted
Came to Pondicherry to clear my head, and somehow an @Airbnb mess has consumed all my energy. We booked a beautiful sea-view property through bnb. The pictures and videos looked great. But when we arrived, it was a completely different story. The place was dirty, mirrors were covered in spots, nothing was properly cleaned, and there was barely any space to move around. It felt nothing like what was advertised. We immediately called the host and explained the situation. He told us we could check out within 5 minutes and receive a full refund. We literally checked out the next minute. Since then, we've been going back and forth with @AirbnbHelp . We recorded the conversation with the host and shared all the evidence, but we're still waiting for a clear confirmation on our refund. Meanwhile, we were left roaming the streets of Pondicherry late at night, trying to find another place to stay. We eventually booked another Airbnb, i think that also costs more. @Airbnb says they're looking into it. But what exactly is there to investigate when we have the conversation, the evidence and the host's own confirmation? We don't expect this kind of experience from a platform like @Airbnb. A trip we planned to unwind has turned into a complete headache. Has anyone else faced something similar with @Airbnb? @AirbnbHelp @Airbnb_in @Airbnb @bchesky @amanpreet_bajaj
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Abhinav Prakash retweeted
Hello folks, today im launching antidraw. an opensource product design & prototyping canvas for your coding agents powered by real code. all my creative outputs for the past few weeks where made in antidraw, including this launch video. i'd love for you to try and give feedback for the alpha.
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Abhinav Prakash retweeted
1/ the tiny design details decide how any software feels. the motion, the states, the UI details you don't notice until they're missing. i build a lot of personal side projects and most of my time goes into crafting those moments.. here are some of my favourites 🧵
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Abhinav Prakash retweeted
this diwali, i am gonna reignite the light of my eyes
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Abhinav Prakash retweeted
this tuesday, I will join the Neatlogs team for a live webinar on everything evals it's going to quite interesting, do join ;)
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Abhinav Prakash retweeted
this isn’t said enough but massive respect to the @opencode folks for building a solution with good free models I used to be very concerned young devs or ones from less wealthy countries would be priced out, glad someone is working to prevent that
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Abhinav Prakash retweeted
My wife is an animal rescuer and today, she got badly assaulted by some people in dommasandra village area @BlrCityPolice we are at varthur police station. Need your support.
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Abhinav Prakash retweeted
1/8 🧵 Outrageous treatment by @AkasaAir on flight QP1904 (BLR–AMD), 20 Oct 2026. My friend, a physiotherapist herself, suffered a complete ACL tear and knee injury. Despite medical clearance and advance notice, ground staff left her crying in pain.
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Abhinav Prakash retweeted
it's confirmed the spark in ur eyes die at 23
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Abhinav Prakash retweeted
I hope I live before I die.
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today we're opening @DetectIfAI to every developer. real-time voice deepfake detection, 24 ms per check. #1 in latency on Podonos' independent benchmark, ahead of companies that raised hundreds of millions. 97.3% of cloned voices caught. API + MCP. link in the comments.
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Abhinav Prakash retweeted
All hijackers are stupid but the extra stupid ones try to hijack a plane full of 180 Israelis all of whom have gone through some of the best military training the world has to offer & an Indian pilot who thinks honor and karma are bigger than his own life. Get well soon Capt'n 🫡
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Abhinav Prakash retweeted
✨ Sherpa by Pocket FM ✨ A week ago, we launched one of the most important products in Pocket FM's history. There are very few serious tools built for writing longform content. With AI at the core, we're now equipped to solve this. Sherpa is your story writing partner.
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Abhinav Prakash retweeted
An opinion: What if we reimagined STEM education without exams, grades, and GPAs? Universities enable learning. Students own it. They get a certificate listing the courses; the world decides how good they are. Why stamp a letter or number on a student? Does this work?
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Sherpa by @PocketFM_App is easily one of the most advanced AI platforms built, give it a spin! For all those who keep asking why otherGPTs can’t do it, you’d start realising what deep context, data & specialisation means.
Introducing Sherpa: the most advanced fiction writing AI We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data. 550K+ creators have produced 2.6M hours of content annualised using it Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 10% of eligible writers on Pocket FM make >$200K One blockbuster produced >$100M in revenue 3 writers have become millionaires in <2 yrs We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories: 1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology 2. World & Characters: It builds out the complete lore, tone, and character psychologies 3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes 4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime 5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks 6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners 7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets Test Sherpa for free here: pocketfm.com/sherpa _____________________________________________ Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function. Sherpa solves this through three core technical leaps: 1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes 2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene. Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts. 3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people. Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked. Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.) _______________________________________________ Owning distribution and creation puts us in a very unique spot. More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows. Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.
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Abhinav Prakash retweeted
Imagine opening the newspaper expecting headlines and getting… nothing. Probably the most expensive blank page you'll see today.
Introducing Sherpa: the most advanced fiction writing AI We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data. 550K+ creators have produced 2.6M hours of content annualised using it Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 10% of eligible writers on Pocket FM make >$200K One blockbuster produced >$100M in revenue 3 writers have become millionaires in <2 yrs We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories: 1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology 2. World & Characters: It builds out the complete lore, tone, and character psychologies 3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes 4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime 5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks 6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners 7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets Test Sherpa for free here: pocketfm.com/sherpa _____________________________________________ Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function. Sherpa solves this through three core technical leaps: 1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes 2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene. Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts. 3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people. Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked. Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.) _______________________________________________ Owning distribution and creation puts us in a very unique spot. More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows. Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.
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Abhinav Prakash retweeted
So pumped this is out in the world. Anyone who's ever wanted to write their own series, this is your sign to begin. Try sherpa ⛰️ : pocketfm.com/sherpa
Introducing Sherpa: the most advanced fiction writing AI We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data. 550K+ creators have produced 2.6M hours of content annualised using it Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 10% of eligible writers on Pocket FM make >$200K One blockbuster produced >$100M in revenue 3 writers have become millionaires in <2 yrs We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories: 1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology 2. World & Characters: It builds out the complete lore, tone, and character psychologies 3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes 4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime 5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks 6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners 7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets Test Sherpa for free here: pocketfm.com/sherpa _____________________________________________ Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function. Sherpa solves this through three core technical leaps: 1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes 2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene. Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts. 3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people. Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked. Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.) _______________________________________________ Owning distribution and creation puts us in a very unique spot. More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows. Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.
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