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[A celebration] Ankha Model: Zy0n7 Voice: 🩷🦋HeavenPink Vtuber🦋🩷 OPEN COMMS Bea model: MayoSplash🔞 (Commission closed) Voice: SuccubusPastryVA 🔞 SFX: OpenNSFW -Introless/Musicless/Textless Full 3 minutes HQ download and more animations on the chuudai page in bio >:3-

Arathyn (3D Commission Open)

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Wolfey 🌷 Commissions CLOSED

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Ninta(ニンタ) Art&Live2D (Vgen comm)

120,887 views • 6 months ago

This character didn't break once across 5 shots, 3 camera angles, and a voice swap. Wan 2.7 is live on OpenArt. Here's what changed: Multimodal control. Text, image, audio, video, all in one generation call. Up to 5 reference inputs plus voice profiles for character locking. Edit existing videos with a sentence. Full creative stack: generate, edit, clone, restyle, extend. The open-source video model that actually acts like a production toolkit.

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OpenArt

2,072,423 views • 4 months ago

Voice AI turn taking is a solved problem. The single most common complaint about voice AI, today, is that agents interrupt too often. But the voice agents I build for myself now respond quickly and interrupt me less often than the people I talk to every day. (I actually measured this.) Mark Backman made a Pipecat AI PR two weeks ago that was the last piece of the puzzle for turn taking so good that I no longer ever think about it. The approach combines three layers of processing: 1. Voice activity detection, with a short (200ms) trigger. 2. A native audio turn detection model that's small, fast, and runs on CPU. This model captures audio nuances like inflection and filler sounds that don't get transcribed. 3. A prompt mixin for the conversation LLM that decides turn completion based on conversation context. None of these are new. We've been using VAD for a long time. We trained the first version of the Pipecat Smart Turn native audio model in December 2024. And we've been experimenting with prompt-based large model turn detection (sometimes called "selective refusal") for more than a year. Now, the Smart Turn model and the SOTA LLMs we're using in voice agents have both gotten so good that using them together feels like we've finally "solved" turn detection. Mark also figured out how to elegantly apply a "single-token tagging" technique to this problem. We sometimes use single-token tagging in place of tool calling, when we need a near-zero latency programmatic trigger. Mark's Pipecat mixin defines three single-token characters and prompts the LLM to output exactly one of them at the beginning of every response. - ✓ means the agent should respond normally (immediately) - ○ is a "short incomplete" - the agent should wait 5 seconds - ◐ is a "long incomplete" - the agent should wait 10 seconds The wait times, and the details of the prompt, are configurable, of course. Watch the video to see me talk to an agent that handles all my various pauses and inflections, plus phrases like "let me think," pretty much the way a person would handle them, in terms of response latency. Also, in the second half of the video, I ask the agent to adjust its response pattern because I'm going to tell it a phone number. This kind of "in-context" adjustment of response wait times is really useful. The LLM in the video is GTP-4.1. We've tested the prompt and single-token adherance with GPT-4.1, Gemini 2.5 Flash, Anthropic Claude Sonnet 4.5, and AWS Nova 2 Pro. Note that older models in all these families (and, in general, smaller open weights models) aren't able to reliably output these single-token tags. But the new models we're using these days are pretty amazing.

Voice AI turn taking is a solved problem. The single most common complaint about voice AI, today, is that agents interrupt too often. But the voice agents I build for myself now respond quickly and interrupt me less often than the people I talk to every day. (I actually measured this.) Mark Backman made a Pipecat AI PR two weeks ago that was the last piece of the puzzle for turn taking so good that I no longer ever think about it. The approach combines three layers of processing: 1. Voice activity detection, with a short (200ms) trigger. 2. A native audio turn detection model that's small, fast, and runs on CPU. This model captures audio nuances like inflection and filler sounds that don't get transcribed. 3. A prompt mixin for the conversation LLM that decides turn completion based on conversation context. None of these are new. We've been using VAD for a long time. We trained the first version of the Pipecat Smart Turn native audio model in December 2024. And we've been experimenting with prompt-based large model turn detection (sometimes called "selective refusal") for more than a year. Now, the Smart Turn model and the SOTA LLMs we're using in voice agents have both gotten so good that using them together feels like we've finally "solved" turn detection. Mark also figured out how to elegantly apply a "single-token tagging" technique to this problem. We sometimes use single-token tagging in place of tool calling, when we need a near-zero latency programmatic trigger. Mark's Pipecat mixin defines three single-token characters and prompts the LLM to output exactly one of them at the beginning of every response. - ✓ means the agent should respond normally (immediately) - ○ is a "short incomplete" - the agent should wait 5 seconds - ◐ is a "long incomplete" - the agent should wait 10 seconds The wait times, and the details of the prompt, are configurable, of course. Watch the video to see me talk to an agent that handles all my various pauses and inflections, plus phrases like "let me think," pretty much the way a person would handle them, in terms of response latency. Also, in the second half of the video, I ask the agent to adjust its response pattern because I'm going to tell it a phone number. This kind of "in-context" adjustment of response wait times is really useful. The LLM in the video is GTP-4.1. We've tested the prompt and single-token adherance with GPT-4.1, Gemini 2.5 Flash, Anthropic Claude Sonnet 4.5, and AWS Nova 2 Pro. Note that older models in all these families (and, in general, smaller open weights models) aren't able to reliably output these single-token tags. But the new models we're using these days are pretty amazing.

kwindla

26,935 views • 6 months ago

R.I.P. Fable 5. Perplexity + Reddit + Opus 4.8 = AI SDR that still booked a $20B hedge fund. (38% reply rate. 30 qualified meetings a month. 0 SDRs hired.) This system reads 1,000+ companies every morning and only writes to the ones already in buying motion... → No more $200K/year SDR payroll for manual account research → No more 10,000 cold emails for a 1% reply → No more bought lists going stale in weeks → No more guessing who's actually ready to buy → No more openers that read like templates Just 1 morning run → the 30 accounts worth calling today. I built it around one rule: never depend on a single model to run. Opus 4.8, GPT 5.5, Mythos, Fable 5 — there will always be a new LLM. Today the US government proved why. The system didn't notice. Here's how it works: → ICP Intelligence (Perplexity does a month of buyer research in 15 minutes) → Account Radar (reads 1,000+ companies every morning, keeps only the matches) → Buying Window (3 live triggers per account: a raise, a key hire, a launch) → Buyer Language (lifts your buyer's exact 5 phrases off Reddit, nothing reads like a template) → Voice Match (drafts a 3-line opener off that signal, in your voice) → Orchestration (Fable runs the whole pass and hands you the 30 to call today) Built on real outbound infrastructure. Model-agnostic. Runs every morning without supervision. Government bans included. Results from my own pipeline: - Reply rate from 1% to 38% - 30 qualified meetings a month, 0 SDRs hired - A $20B hedge fund and a $15B fund booked - $340K in new pipeline last quarter Want the complete build? Like + comment "RECON" + repost, and I'll DM it to you. (must be following)

R.I.P. Fable 5. Perplexity + Reddit + Opus 4.8 = AI SDR that still booked a $20B hedge fund. (38% reply rate. 30 qualified meetings a month. 0 SDRs hired.) This system reads 1,000+ companies every morning and only writes to the ones already in buying motion... → No more $200K/year SDR payroll for manual account research → No more 10,000 cold emails for a 1% reply → No more bought lists going stale in weeks → No more guessing who's actually ready to buy → No more openers that read like templates Just 1 morning run → the 30 accounts worth calling today. I built it around one rule: never depend on a single model to run. Opus 4.8, GPT 5.5, Mythos, Fable 5 — there will always be a new LLM. Today the US government proved why. The system didn't notice. Here's how it works: → ICP Intelligence (Perplexity does a month of buyer research in 15 minutes) → Account Radar (reads 1,000+ companies every morning, keeps only the matches) → Buying Window (3 live triggers per account: a raise, a key hire, a launch) → Buyer Language (lifts your buyer's exact 5 phrases off Reddit, nothing reads like a template) → Voice Match (drafts a 3-line opener off that signal, in your voice) → Orchestration (Fable runs the whole pass and hands you the 30 to call today) Built on real outbound infrastructure. Model-agnostic. Runs every morning without supervision. Government bans included. Results from my own pipeline: - Reply rate from 1% to 38% - 30 qualified meetings a month, 0 SDRs hired - A $20B hedge fund and a $15B fund booked - $340K in new pipeline last quarter Want the complete build? Like + comment "RECON" + repost, and I'll DM it to you. (must be following)

Aryan Mahajan

31,355 views • 2 months ago

NEW: AssemblyAI Handles 4X More Voice Data Per Day Than YouTube "Our TAM has just increased by 100X" CEO Dylan Fox (Dylan Fox) One of the fastest growing categories in AI, voice powers note-taking, healthcare, coding agents, call centers, AI companions, drive-thru ordering, consumer electronics & humanoid robots. AssemblyAI is the infrastructure underneath it. Powering billion-dollar companies like Granola & Commure, + Tolans & Ciro AI. Stats: › 120M+ voice conversations a week at peak › 2M+ hours of voice a day, 4X YouTube's daily volume › Weekly conversations up 800% in 3 years › 1M+ developers, 40% signed up last year › ~100M API calls a day › ~80 employees AssemblyAI was 1 of 6 companies in YC's first AI batch in 2017, run by Daniel Gross. Backed by Accel, Insight Partners, YC, Smith Point Capital, Nat Friedman, Daniel Gross, Patrick & John Collison We cover: › The McDonald's drive-thru has no idea it's McDonald's › Why 75% of a voice model is the data you train it on › Why humanoid robots can't tell who's talking to them › Whether voice agents should have disclosures 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Dylan Fox, Founder & CEO at Assembly AI (01:06) AssemblyAI's voice traffic beat YouTube by 4x (03:33) $100K in GPU credits that started it all (06:39) Why Voice AI is inflecting right now (10:11) AssemblyAI's infrastructure-first strategy (12:41) Handling 120 million weekly calls (14:49) How AI agents are rewriting Internal Ops at AssemblyAI (17:05) The Sovereign AI problem nobody's solved for voice (18:26) Is the Keyboard and Mouse finally dying? (22:33) The problem Humanoid Robotics hasn't solved yet (25:14) The real problem behind translating languages (29:27) Can AI actually talk to animals? (31:24) The real reason Open-Source benchmarks lie to you (34:20) Building websites in chat rooms as a kid (36:48) On-Device AI models are about to change everything (43:03) The people who shaped Dylan's career

NEW: AssemblyAI Handles 4X More Voice Data Per Day Than YouTube "Our TAM has just increased by 100X" CEO Dylan Fox (Dylan Fox) One of the fastest growing categories in AI, voice powers note-taking, healthcare, coding agents, call centers, AI companions, drive-thru ordering, consumer electronics & humanoid robots. AssemblyAI is the infrastructure underneath it. Powering billion-dollar companies like Granola & Commure, + Tolans & Ciro AI. Stats: › 120M+ voice conversations a week at peak › 2M+ hours of voice a day, 4X YouTube's daily volume › Weekly conversations up 800% in 3 years › 1M+ developers, 40% signed up last year › ~100M API calls a day › ~80 employees AssemblyAI was 1 of 6 companies in YC's first AI batch in 2017, run by Daniel Gross. Backed by Accel, Insight Partners, YC, Smith Point Capital, Nat Friedman, Daniel Gross, Patrick & John Collison We cover: › The McDonald's drive-thru has no idea it's McDonald's › Why 75% of a voice model is the data you train it on › Why humanoid robots can't tell who's talking to them › Whether voice agents should have disclosures 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Dylan Fox, Founder & CEO at Assembly AI (01:06) AssemblyAI's voice traffic beat YouTube by 4x (03:33) $100K in GPU credits that started it all (06:39) Why Voice AI is inflecting right now (10:11) AssemblyAI's infrastructure-first strategy (12:41) Handling 120 million weekly calls (14:49) How AI agents are rewriting Internal Ops at AssemblyAI (17:05) The Sovereign AI problem nobody's solved for voice (18:26) Is the Keyboard and Mouse finally dying? (22:33) The problem Humanoid Robotics hasn't solved yet (25:14) The real problem behind translating languages (29:27) Can AI actually talk to animals? (31:24) The real reason Open-Source benchmarks lie to you (34:20) Building websites in chat rooms as a kid (36:48) On-Device AI models are about to change everything (43:03) The people who shaped Dylan's career

Molly O’Shea

326,534 views • 20 days ago

We partnered with artists, designers, and builders to create new AI tools that solve real problems in their creative workflows. Here’s what’s new: — Introducing Google Pics in Google Workspace: A brand-new image creation & editing tool. Move and resize objects, add text, and translate just by hovering and clicking — Big updates to صافي النيه😉: 1) You can now create with Gemini Omni Flash in Google Flow 2) Google Flow Agent is a multi-step creative partner that reasons and plans complex tasks with you. 3) Google Flow tools are custom tools you can “vibe code” for animations, video effects, text layering & more — Design live with Stitch by Google: Now, you can use text or voice prompts to edit layouts in real time then export those designs straight to code — More creative control in صافي النيه😉Music: Edit songs section by section, remix the style of full songs, and create music videos with our new Gemini Omni Flash model

Google AI

13,954,011 views • 3 months ago

You have to really give it to OpenAI because Sora 2 is very impressive on a lot of fronts: - high quality video model with great physics - high quality audio in each video - high character consistency - multiple characters in one scene - accurate characters voice - social platform attached to it Before today the best AI video models were dominated by Chinese companies like ByteDance and Kuaishou and Google with Veo3. ByteDance makes TikTok, Kuaishou makes Kwai (similar app) and Google has YouTube to train on But none of these models had great character consistency, if it was a feature at all, let alone multiple characters in one scene. Generally you'd make a video and the face would slowly change into someone else, just not good On top of that Google was struggling with allowing people to upload characters scared it'd get abused for deep fakes, and just generally nerfing their model so you can't really use it for anything OpenAI solved that by re-thinking ownership over your characters smartly with Cameo, which is essentially "train yourself as a AI model" which we've all been doing in our apps for years, but in a more smart way, where you can control if only you make content with your appearance, or others too They've also added voice training to it immediately, which people would have to do separate on for ex ElevenLabs before On top of that the social platform aspect: Google's Veo 3 didn't have ANY community at all, while the Chinese video models did, but it was all more like weekly themed contests to win free credits, they never really managed to make it more than that, and it kinda stayed in this nerdy AI hacking vibe This vibe fits how hard it was/is to simply make a video featuring you or your friends with proper voice and audio and everything that Sora 2 does for you. You'd have to go to ElevenLabs to train your voices, then go to for ex Photo AI to train yourself as a person, then make videos, then add audio and voices, then edit them together, a lot of work! We don't know if Sora 2's social platform features will actually be used or take off, but it's a real cool experiment in trying to find a way to build a community around AI in a more Instagram-like way Being able to tag your friends and then add them as multiple characters is innovative in both the social and technical aspect So TL;DR OpenAI essentially took a lot of stuff that was already technically possible, then added new things that weren't possible yet, and then put it all together in a very friendly interface that even my mom can use, with generation times of just a few minutes which is extremely fast if you think of the pipeline behind it (multiple video generation + voice + audio etc.) And also importantly, it doesn't look like they nerfed it much for safety which is also very cool considering the legal risks So yes very very very impressive

You have to really give it to OpenAI because Sora 2 is very impressive on a lot of fronts: - high quality video model with great physics - high quality audio in each video - high character consistency - multiple characters in one scene - accurate characters voice - social platform attached to it Before today the best AI video models were dominated by Chinese companies like ByteDance and Kuaishou and Google with Veo3. ByteDance makes TikTok, Kuaishou makes Kwai (similar app) and Google has YouTube to train on But none of these models had great character consistency, if it was a feature at all, let alone multiple characters in one scene. Generally you'd make a video and the face would slowly change into someone else, just not good On top of that Google was struggling with allowing people to upload characters scared it'd get abused for deep fakes, and just generally nerfing their model so you can't really use it for anything OpenAI solved that by re-thinking ownership over your characters smartly with Cameo, which is essentially "train yourself as a AI model" which we've all been doing in our apps for years, but in a more smart way, where you can control if only you make content with your appearance, or others too They've also added voice training to it immediately, which people would have to do separate on for ex ElevenLabs before On top of that the social platform aspect: Google's Veo 3 didn't have ANY community at all, while the Chinese video models did, but it was all more like weekly themed contests to win free credits, they never really managed to make it more than that, and it kinda stayed in this nerdy AI hacking vibe This vibe fits how hard it was/is to simply make a video featuring you or your friends with proper voice and audio and everything that Sora 2 does for you. You'd have to go to ElevenLabs to train your voices, then go to for ex Photo AI to train yourself as a person, then make videos, then add audio and voices, then edit them together, a lot of work! We don't know if Sora 2's social platform features will actually be used or take off, but it's a real cool experiment in trying to find a way to build a community around AI in a more Instagram-like way Being able to tag your friends and then add them as multiple characters is innovative in both the social and technical aspect So TL;DR OpenAI essentially took a lot of stuff that was already technically possible, then added new things that weren't possible yet, and then put it all together in a very friendly interface that even my mom can use, with generation times of just a few minutes which is extremely fast if you think of the pipeline behind it (multiple video generation + voice + audio etc.) And also importantly, it doesn't look like they nerfed it much for safety which is also very cool considering the legal risks So yes very very very impressive

@levelsio

178,100 views • 10 months ago

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre

Qiusheng Wu

18,075 views • 2 months ago

I'm making over $10K a month selling AI services. I got there in under a year with a three-stage model: a free assessment, a paid assessment, and an AI Concierge retainer. My lowest paying client is $1,200 a month. My highest is $2,000. Here's the entire model: 1) Stage one: a free 15-minute mini assessment. One pain point, one prescribed fix. Do 1 to 3 max, purely for testimonials. 2) Stage two: the paid assessment. 45 minutes, 3 to 7 opportunities scored on effort vs impact, sold for $1,000 to $2,000. (I open sourced my paid assessment template. It's yours (free) at 3) Do the ROI math in their words. $250 an hour times 5 hours a week in email is $1,250 a week. Cut it to 1 hour and they can't argue. 4) Stage three: AI Concierge. Two 45-minute calls a month at $1,000 to $2,000. That's $750 to $1,250 an hour. 5) Add unlimited Voxer access. Feels like an AI expert in their pocket, so they pay top dollar. They barely use it. 6) Every call runs AOA: Audit, Optimize, Automate. Cut the 14-step process to 11, then turn it into a Claude skill. 7) Standardize on one ecosystem. Everything runs in Claude Cowork building Claude skills. No custom stack per client, no vendor lock-in. 8) Target owner-led service businesses doing $3M to $10M. Repetitive work, visible bottlenecks, real budget. 9) Your first client is in your phone. Text 10 business owners you know. 10 texts equals 5 to 6 free assessments, 2 to 3 paid, 1 concierge client. 10) Raise your price $250 with every yes. My next client won't be under $2,500 a month. Two things that make this work: 1) Boundaries are the offer. Free stops at the roadmap, concierge stops at two calls. Everything else is billed separately. 2) Every stage sells the next one. The free assessment promises seven more fixes. The paid report walkthrough pitches the retainer. Full breakdown below. (also available on the Build With AI podcast wherever you get your pods)

I'm making over $10K a month selling AI services. I got there in under a year with a three-stage model: a free assessment, a paid assessment, and an AI Concierge retainer. My lowest paying client is $1,200 a month. My highest is $2,000. Here's the entire model: 1) Stage one: a free 15-minute mini assessment. One pain point, one prescribed fix. Do 1 to 3 max, purely for testimonials. 2) Stage two: the paid assessment. 45 minutes, 3 to 7 opportunities scored on effort vs impact, sold for $1,000 to $2,000. (I open sourced my paid assessment template. It's yours (free) at 3) Do the ROI math in their words. $250 an hour times 5 hours a week in email is $1,250 a week. Cut it to 1 hour and they can't argue. 4) Stage three: AI Concierge. Two 45-minute calls a month at $1,000 to $2,000. That's $750 to $1,250 an hour. 5) Add unlimited Voxer access. Feels like an AI expert in their pocket, so they pay top dollar. They barely use it. 6) Every call runs AOA: Audit, Optimize, Automate. Cut the 14-step process to 11, then turn it into a Claude skill. 7) Standardize on one ecosystem. Everything runs in Claude Cowork building Claude skills. No custom stack per client, no vendor lock-in. 8) Target owner-led service businesses doing $3M to $10M. Repetitive work, visible bottlenecks, real budget. 9) Your first client is in your phone. Text 10 business owners you know. 10 texts equals 5 to 6 free assessments, 2 to 3 paid, 1 concierge client. 10) Raise your price $250 with every yes. My next client won't be under $2,500 a month. Two things that make this work: 1) Boundaries are the offer. Free stops at the roadmap, concierge stops at two calls. Everything else is billed separately. 2) Every stage sells the next one. The free assessment promises seven more fixes. The paid report walkthrough pitches the retainer. Full breakdown below. (also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

43,270 views • 6 days ago

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

Yarchi

106,174 views • 2 months ago

Atomic Agent beat Hermes on GAIA: 69.8% vs 58.5%, and it was 1.6x faster! We ran both agents through the full GAIA Level 1 benchmark, 53 real-world tasks, same 4-bit qwen-3.6-35b on the same Apple M4 Max. Results: ✦ Atomic Agent: 37 of 53 solved, done in 3h 12m ✦ Hermes Agent: 31 of 53 solved, took 5h 10m Atomic solved 6 more tasks and finished nearly 2 hours sooner. Hermes ran into the 900s timeout on 7 tasks; Atomic on just 2. Hermes burned 71% of its total time on tasks it still failed, Atomic, 48%. Where it showed: ✦ Audre Lorde poem, which stanza is indented: Atomic pushed through a dead source, switched tools, and answered in 7.6 min. Hermes ran the full clock and returned a blank. ✦ Vietnamese specimens, which city they ended up in: Atomic pulled it from the first source and normalized the answer in 33s. Hermes spent 7.3 min and never answered. ✦ The dinosaur featured-article nominator: Atomic walked the Wikipedia chain to "FunkMonk" in 57s. Hermes guessed a wrong name after 11 min. Atomic keeps a byte-stable prompt prefix, so llama-server reuses the KV-cache instead of re-encoding the whole context every turn, and it emits one JSON array of tool calls per inference, then compresses results back instead of pasting them in full, so the context never balloons and a small model stays sharp deep into a task. On top of that a no-progress guard vetoes repeated identical tool calls (warn at 3, hard veto at 5) and forces a reply, so Atomic never sinks 15 minutes into re-scanning one page the way Hermes did. Both agents missed some of the same questions, and on a few Hermes got there and Atomic did not, usually format slips where Atomic computed the right number but printed the working instead of the bare value. But on identical hardware and identical weights, the runtime that reuses its cache and refuses to spin came out ahead on accuracy and speed. Getting this from the runtime alone is wild. Run the same 53 GAIA tasks on Atomic Agent!

Atomic Agent beat Hermes on GAIA: 69.8% vs 58.5%, and it was 1.6x faster! We ran both agents through the full GAIA Level 1 benchmark, 53 real-world tasks, same 4-bit qwen-3.6-35b on the same Apple M4 Max. Results: ✦ Atomic Agent: 37 of 53 solved, done in 3h 12m ✦ Hermes Agent: 31 of 53 solved, took 5h 10m Atomic solved 6 more tasks and finished nearly 2 hours sooner. Hermes ran into the 900s timeout on 7 tasks; Atomic on just 2. Hermes burned 71% of its total time on tasks it still failed, Atomic, 48%. Where it showed: ✦ Audre Lorde poem, which stanza is indented: Atomic pushed through a dead source, switched tools, and answered in 7.6 min. Hermes ran the full clock and returned a blank. ✦ Vietnamese specimens, which city they ended up in: Atomic pulled it from the first source and normalized the answer in 33s. Hermes spent 7.3 min and never answered. ✦ The dinosaur featured-article nominator: Atomic walked the Wikipedia chain to "FunkMonk" in 57s. Hermes guessed a wrong name after 11 min. Atomic keeps a byte-stable prompt prefix, so llama-server reuses the KV-cache instead of re-encoding the whole context every turn, and it emits one JSON array of tool calls per inference, then compresses results back instead of pasting them in full, so the context never balloons and a small model stays sharp deep into a task. On top of that a no-progress guard vetoes repeated identical tool calls (warn at 3, hard veto at 5) and forces a reply, so Atomic never sinks 15 minutes into re-scanning one page the way Hermes did. Both agents missed some of the same questions, and on a few Hermes got there and Atomic did not, usually format slips where Atomic computed the right number but printed the working instead of the bare value. But on identical hardware and identical weights, the runtime that reuses its cache and refuses to spin came out ahead on accuracy and speed. Getting this from the runtime alone is wild. Run the same 53 GAIA tasks on Atomic Agent!

Atomic Agent

111,357 views • 27 days ago

kimi k3 vs gpt 5.6 sol vs fable 5 vs grok 4.5 Kimi.ai just dropped kimi k3 – a 2.8t param native multimodal model, the first open 3t-class release. key facts: • 1m token context. stable latentmoe activating 16 of 896 experts, built on kimi delta attention (kda) and attention residuals • quantization-aware training from the sft stage onward – mxfp4 weights, mxfp8 activations. moonshot claims ~2.5x scaling efficiency over k2 • max thinking effort by default. low- and high-effort modes are "coming in updates" – there is no way to turn the thinking down today, and you feel it in every run • pricing: $0.30/mtok cache-hit input, $3.00/mtok cache-miss, $15.00/mtok output. claims >90% cache hit rate on coding workloads • benchmarks: swe marathon 42.0 (1st – fable 5: 35.0, sol: 39.0, opus 4.8: 40.0), terminal bench 2.1 88.3, browsecomp 91.2 (1st), program bench 77.8 (1st), gpqa-diamond 93.5. loses frontierswe 81.2 vs fable's 86.6, and deepswe 67.5 vs sol's 73.0 our test – 3 prompts, single-file html, Three.js, fully procedural, no assets: 1. photorealistic european roulette wheel – 37 pockets in the real sequence, mahogany clearcoat bowl, chrome turret, diamond deflectors, flick-to-spin, ball that spirals inward and settles on a mathematically real number 2. las vegas slot machine – 3 reels behind transmissive glass, drag the chrome lever to play, mechanical odometer counters modelled in 3d, coin physics on win 3. full pinball table – 6.5° tilted playfield, flipper impulse physics, spline ramps, drop targets, 6 bumpers, mechanical score reels in the backbox we ran the test on AI/ML API platform results: - cost #1 grok 4.5 – $0.30 #2 kimi k3 – $0.71 #3 gpt 5.6 sol – $2.05 #4 fable 5 – $7.69 - tokens #1 grok 4.5 – 34,241 #2 gpt 5.6 sol – 51,748 #3 fable 5 – 144,126 #4 kimi k3 – 157,999 - lines of code #1 gpt 5.6 sol – 3,054 #2 grok 4.5 – 3,047 #3 kimi k3 – 2,255 #4 fable 5 – 1,950 - generation time #1 grok 4.5 – 5.1 min #2 gpt 5.6 sol – 22.0 min #3 fable 5 – 31.5 min #4 kimi k3 – 75.6 min observations: • kimi k3 is cheap and it is slow. 75.6 minutes across three prompts against grok's 5.1. it is 2.4x grok's price and 15x grok's wall clock. the roulette took 15 min, the slot 18, the pinball 42 • it failed 2 of 3. only the roulette works. the slot machine has reel cutouts on both faces of the cabinet and the symbols face backwards – you can only read your spin by walking around to the rear of the machine. the pinball table stands vertically on its edge with the legs floating detached beside it. • 81% of kimi's output tokens are reasoning, not code. grok: 22%. you are not paying for a bigger answer, you are paying for a longer argument with itself • price per 100 shipped lines – grok $0.010, kimi $0.031, sol $0.067, fable $0.394. a 39x spread for the same three files kimi k3's code quality: upsides: • the roulette is genuinely good – procedural wood grain with real specular breakup, correct european sequence (0-32-15-19-4...), chrome turret, diamond deflectors, clean console • the pinball artwork is the best in the test – a synthwave "nova strike / deep space" field with six individually coloured neon bumper rings, a retro sun on a grid horizon, a nova burst, and a scoring legend printed on the apron. no other model printed the rules on the machine. it is a beautiful texture on a broken object • physics reasoning is real – it derived a 480hz substep for the collider, worked out ball settle conditions and termination guarantees, and checked every ramp exit vector by hand before writing any of it • it is the only model that saw the importmap trap coming. sol shipped a blank white page twice because three.js addons import the bare specifier 'three' and die without an import map downsides: • it dodged that trap on the slot by loading three.js r128 through classic script tags – a 2021 build with no working transmission. its slot glass rendered fully opaque and buried all three reels behind a white pane. the code asks for transmission: 0.93, ior: 1.5 – correct, and silently ignored by a renderer that predates the feature • after 42 minutes and 212k characters of reasoning, the pinball cabinet is not assembled. the table stands vertically on its edge like a wardrobe – the prompt asked for 6.5° from horizontal, it delivered 90°. the legs float detached in the void beside it. head-on it photographs beautifully; orbit ten degrees and it is a painted slab with four chrome rods hovering nearby • the playfield z-fights with the glass – hard black banding across the whole field as soon as you pull the camera back a note on the pinball, in fairness to kimi: nobody passed it. every model shipped broken ball physics and controls you cannot trust. it is the hardest prompt we have run and the whole field failed it, each in its own way kimi k3 reasons better than anything else here and it shows exactly where reasoning pays – physics constants, sequences, edge cases, traps the others walked into follow thehype. for 24/7 ai news, analysis and breakdowns

kimi k3 vs gpt 5.6 sol vs fable 5 vs grok 4.5 Kimi.ai just dropped kimi k3 – a 2.8t param native multimodal model, the first open 3t-class release. key facts: • 1m token context. stable latentmoe activating 16 of 896 experts, built on kimi delta attention (kda) and attention residuals • quantization-aware training from the sft stage onward – mxfp4 weights, mxfp8 activations. moonshot claims ~2.5x scaling efficiency over k2 • max thinking effort by default. low- and high-effort modes are "coming in updates" – there is no way to turn the thinking down today, and you feel it in every run • pricing: $0.30/mtok cache-hit input, $3.00/mtok cache-miss, $15.00/mtok output. claims >90% cache hit rate on coding workloads • benchmarks: swe marathon 42.0 (1st – fable 5: 35.0, sol: 39.0, opus 4.8: 40.0), terminal bench 2.1 88.3, browsecomp 91.2 (1st), program bench 77.8 (1st), gpqa-diamond 93.5. loses frontierswe 81.2 vs fable's 86.6, and deepswe 67.5 vs sol's 73.0 our test – 3 prompts, single-file html, Three.js, fully procedural, no assets: 1. photorealistic european roulette wheel – 37 pockets in the real sequence, mahogany clearcoat bowl, chrome turret, diamond deflectors, flick-to-spin, ball that spirals inward and settles on a mathematically real number 2. las vegas slot machine – 3 reels behind transmissive glass, drag the chrome lever to play, mechanical odometer counters modelled in 3d, coin physics on win 3. full pinball table – 6.5° tilted playfield, flipper impulse physics, spline ramps, drop targets, 6 bumpers, mechanical score reels in the backbox we ran the test on AI/ML API platform results: - cost #1 grok 4.5 – $0.30 #2 kimi k3 – $0.71 #3 gpt 5.6 sol – $2.05 #4 fable 5 – $7.69 - tokens #1 grok 4.5 – 34,241 #2 gpt 5.6 sol – 51,748 #3 fable 5 – 144,126 #4 kimi k3 – 157,999 - lines of code #1 gpt 5.6 sol – 3,054 #2 grok 4.5 – 3,047 #3 kimi k3 – 2,255 #4 fable 5 – 1,950 - generation time #1 grok 4.5 – 5.1 min #2 gpt 5.6 sol – 22.0 min #3 fable 5 – 31.5 min #4 kimi k3 – 75.6 min observations: • kimi k3 is cheap and it is slow. 75.6 minutes across three prompts against grok's 5.1. it is 2.4x grok's price and 15x grok's wall clock. the roulette took 15 min, the slot 18, the pinball 42 • it failed 2 of 3. only the roulette works. the slot machine has reel cutouts on both faces of the cabinet and the symbols face backwards – you can only read your spin by walking around to the rear of the machine. the pinball table stands vertically on its edge with the legs floating detached beside it. • 81% of kimi's output tokens are reasoning, not code. grok: 22%. you are not paying for a bigger answer, you are paying for a longer argument with itself • price per 100 shipped lines – grok $0.010, kimi $0.031, sol $0.067, fable $0.394. a 39x spread for the same three files kimi k3's code quality: upsides: • the roulette is genuinely good – procedural wood grain with real specular breakup, correct european sequence (0-32-15-19-4...), chrome turret, diamond deflectors, clean console • the pinball artwork is the best in the test – a synthwave "nova strike / deep space" field with six individually coloured neon bumper rings, a retro sun on a grid horizon, a nova burst, and a scoring legend printed on the apron. no other model printed the rules on the machine. it is a beautiful texture on a broken object • physics reasoning is real – it derived a 480hz substep for the collider, worked out ball settle conditions and termination guarantees, and checked every ramp exit vector by hand before writing any of it • it is the only model that saw the importmap trap coming. sol shipped a blank white page twice because three.js addons import the bare specifier 'three' and die without an import map downsides: • it dodged that trap on the slot by loading three.js r128 through classic script tags – a 2021 build with no working transmission. its slot glass rendered fully opaque and buried all three reels behind a white pane. the code asks for transmission: 0.93, ior: 1.5 – correct, and silently ignored by a renderer that predates the feature • after 42 minutes and 212k characters of reasoning, the pinball cabinet is not assembled. the table stands vertically on its edge like a wardrobe – the prompt asked for 6.5° from horizontal, it delivered 90°. the legs float detached in the void beside it. head-on it photographs beautifully; orbit ten degrees and it is a painted slab with four chrome rods hovering nearby • the playfield z-fights with the glass – hard black banding across the whole field as soon as you pull the camera back a note on the pinball, in fairness to kimi: nobody passed it. every model shipped broken ball physics and controls you cannot trust. it is the hardest prompt we have run and the whole field failed it, each in its own way kimi k3 reasons better than anything else here and it shows exactly where reasoning pays – physics constants, sequences, edge cases, traps the others walked into follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

2,180,235 views • 1 month ago

Karpathy just proved the structure beats the model - mapped the whole path from loops to graphs into a 15-page PDF: he pointed one agent at code he'd already hand-optimized. 700 experiments in two days, then he said the single loop was never the target here's the full system: step 1 → write the scorer before anything else. one number, automatic, under 10 minutes. no scorer, no loop step 2 → split the files. one the agent edits, one holding the scorer it can never reach step 3 → fix the wall clock per attempt. comparability comes from the budget, not the attempts step 4 → write the never-stop line verbatim or your agent becomes a slower human step 5 → add a second agent only after the first ships real commits. one git worktree each or they overwrite each other step 6 → at thirty agents, isolation costs more than it saves. that's when they need one shared DAG instead of thirty private histories 2.02 hours to 1.80 hours on code its author had already tuned. 90,900 stars in four months. read this PDF and paste it into your Claude, you won't regret it bookmark, then read the article on building graphs from scratch ↓

Karpathy just proved the structure beats the model - mapped the whole path from loops to graphs into a 15-page PDF: he pointed one agent at code he'd already hand-optimized. 700 experiments in two days, then he said the single loop was never the target here's the full system: step 1 → write the scorer before anything else. one number, automatic, under 10 minutes. no scorer, no loop step 2 → split the files. one the agent edits, one holding the scorer it can never reach step 3 → fix the wall clock per attempt. comparability comes from the budget, not the attempts step 4 → write the never-stop line verbatim or your agent becomes a slower human step 5 → add a second agent only after the first ships real commits. one git worktree each or they overwrite each other step 6 → at thirty agents, isolation costs more than it saves. that's when they need one shared DAG instead of thirty private histories 2.02 hours to 1.80 hours on code its author had already tuned. 90,900 stars in four months. read this PDF and paste it into your Claude, you won't regret it bookmark, then read the article on building graphs from scratch ↓

Carnage

53,824 views • 23 days ago

how to turn iMessage into an AI executive assistant 1. give lindy your phone number and google account. two steps. that's the entire setup. 2. wake up to a daily brief. your meetings, the weather, 63 emails triaged overnight, replies already drafted. you barely open gmail anymore. 3. get meeting prep 15 minutes before every call. who you're meeting, what you talked about last time, what they need from you. 4. message teammates mid-meeting without leaving the call. you say it out loud. lindy sends the slack, creates the google doc, notifies the team. 5. send a screenshot of anything and it acts on it. party invite. invoice. voice note. one message and it handles it. 6. update your CRM after every call automatically. tell it once. done forever. 7. ask it anything about your past meetings and emails. "what did that company say they needed?" it knows. your inbox is its memory. 8. let it catch the stuff you'd miss. "your dinner reservation is at a restaurant closed on tuesdays. want me to move it?" the best executive assistants don't wait to be asked because they just handle it. that's what kinda Lindy feels like. not sure why more people aren't talking about products like these i liked it so much i invested in the biz. this is for people who find openclaw daunting and dont want to deal with the security issues episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) watch

how to turn iMessage into an AI executive assistant 1. give lindy your phone number and google account. two steps. that's the entire setup. 2. wake up to a daily brief. your meetings, the weather, 63 emails triaged overnight, replies already drafted. you barely open gmail anymore. 3. get meeting prep 15 minutes before every call. who you're meeting, what you talked about last time, what they need from you. 4. message teammates mid-meeting without leaving the call. you say it out loud. lindy sends the slack, creates the google doc, notifies the team. 5. send a screenshot of anything and it acts on it. party invite. invoice. voice note. one message and it handles it. 6. update your CRM after every call automatically. tell it once. done forever. 7. ask it anything about your past meetings and emails. "what did that company say they needed?" it knows. your inbox is its memory. 8. let it catch the stuff you'd miss. "your dinner reservation is at a restaurant closed on tuesdays. want me to move it?" the best executive assistants don't wait to be asked because they just handle it. that's what kinda Lindy feels like. not sure why more people aren't talking about products like these i liked it so much i invested in the biz. this is for people who find openclaw daunting and dont want to deal with the security issues episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) watch

GREG ISENBERG

248,935 views • 4 months ago

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Himanshu Kumar

13,599 views • 4 months ago

🚨 The Next Evolution of AI Music is Here 🚨 We haven’t been standing still. We’ve been building at an incredible pace, with laser-sharp focus, pushing the boundaries of AI-powered music creation like never before. Our latest upgrade isn’t just more powerful—it’s more versatile, precise, and deeply creative than anything before. 🎶 Proof is in the sound: This song was generated from a simple prompt—“Blues with slight Arabian influence about a man lost in the desert searching for his bride.” Listen to the end and hear how $SUEDE AI captures emotion, style, and storytelling like never before. But this is just the beginning. Our new features are built for artists who want total creative control. Get extremely granular with how you craft and shape your sound: 🎛️ Full Production Control – Download an entire pack of every isolated instrument. 🎤 Use Your Own Voice – Or someone else’s. 📝 Exact Lyrics, Your Way – Have your words set to music seamlessly. 🎶 Reference Songs – Upload one for style analysis, extraction, and modeling—or simply note a publicly available track. 🔊 Text-to-Speech & AI Vocalists – Shape voices like never before. 🎼 Melody Collaboration – Upload a melody idea and let others build around it—or vice versa. However, due to cost considerations, we’ve capped it at 3 free songs per trial until subscription payments roll out in the next day or two. We’ll be launching a new payment gateway soon, so stay tuned for more details. And remember, all of this is powered by the $SUEDE token. It fuels the entire ecosystem, allowing artists to generate, own, and monetize their work like never before. We’re still working out a few kinks—like image generation—but prepare to be impressed. A major post is coming soon, breaking down these game-changing features and the revenue model behind them. Thread dropping soon. Turn notifications on. $SUEDE powers the future of culture. #SuedeAI #Web3Music

🚨 The Next Evolution of AI Music is Here 🚨 We haven’t been standing still. We’ve been building at an incredible pace, with laser-sharp focus, pushing the boundaries of AI-powered music creation like never before. Our latest upgrade isn’t just more powerful—it’s more versatile, precise, and deeply creative than anything before. 🎶 Proof is in the sound: This song was generated from a simple prompt—“Blues with slight Arabian influence about a man lost in the desert searching for his bride.” Listen to the end and hear how $SUEDE AI captures emotion, style, and storytelling like never before. But this is just the beginning. Our new features are built for artists who want total creative control. Get extremely granular with how you craft and shape your sound: 🎛️ Full Production Control – Download an entire pack of every isolated instrument. 🎤 Use Your Own Voice – Or someone else’s. 📝 Exact Lyrics, Your Way – Have your words set to music seamlessly. 🎶 Reference Songs – Upload one for style analysis, extraction, and modeling—or simply note a publicly available track. 🔊 Text-to-Speech & AI Vocalists – Shape voices like never before. 🎼 Melody Collaboration – Upload a melody idea and let others build around it—or vice versa. However, due to cost considerations, we’ve capped it at 3 free songs per trial until subscription payments roll out in the next day or two. We’ll be launching a new payment gateway soon, so stay tuned for more details. And remember, all of this is powered by the $SUEDE token. It fuels the entire ecosystem, allowing artists to generate, own, and monetize their work like never before. We’re still working out a few kinks—like image generation—but prepare to be impressed. A major post is coming soon, breaking down these game-changing features and the revenue model behind them. Thread dropping soon. Turn notifications on. $SUEDE powers the future of culture. #SuedeAI #Web3Music

Suede Labs

17,424 views • 1 year ago

Porter Stansberry turned $36,000 into $3,000,000,000 by writing financial research… after his future business partner fired him... Here's what I learned from Porter Stansberry about writing and selling financial research: 1. Hierarchy of Information Value: In the crowded financial information space, Porter stands out by providing clear, actionable advice, pushing against conventional views. “The news is free because it tells you what happened. Newsletters are expensive because they tell you what to do about it.” 2. Masterful Financial Storytelling: Porter's a master storyteller. He unearths captivating historical tales, dripping with human drama, and seamlessly melds them with modern investment concepts. A voracious reader, Porter often devours a book a day. These stories make his content addictive, easy to grasp, and unforgettable. 3. Creating 'Eureka Moments': Porter leads his readers to conclusions without explicitly stating them. For instance, consider his story about Shelby Davis, who transformed $50,000 into a billion dollars through passive investments in property and casualty insurance stocks. Porter doesn't tell you it's a fantastic business model; he shows you. These "Eureka Moments" are not only enjoyable to read but also encourage independent thinking, fostering loyal readership. 4. In-depth Research: Porter's best investment ideas come from his network of industry specialists he’s in constant contact with. These include a deep talent roster within his companies and a network of external contacts he shares ideas with. Porter strives to cover only topics where he’s had 3-4 conversations with industry insiders, adding depth and credibility to his work. 5. Clear and Simple Writing: To make his ideas more accessible, Porter writes to a novice reading level. He focuses on straightforward, active language (No passive voice), using short, simple sentences. Porter meticulously edits and rewrites. His epic 'End of America' went through 15 versions before selling over 500,000 subscriptions. Porter also prefers clear sentences over excessive visuals. 6. Effective Promotion: Porter’s marketing and research follow the same approach: every sentence engages and adds value. His campaigns tackle big, important issues that affect readers’ lives. Successful campaigns are amplified through collaboration with other publishers. 7. Ethical Standards: For Porter, having a great track record of recommendations is the ultimate skin-in-the-game. Not trading in the securities he covers helps him maintain objectivity, avoid conflicts, and lessen legal risk. 8. Full Tilt Dedication: Porter swears by one way of doing investment research: "Full tilt." His success is a testament to his intense work ethic and decades of relentless trial and error. Don't miss the full interview with Porter Stansberry below, where he delves into his personal journey, the challenges of Marketwise's IPO, and his perspective on current market dynamics. For past stories on Porter, a link to a YouTube interview, and a transcript, check out the replies. Follow me for more insights on finance and investing! TIMESTAMPS: Going Viral in Zimbabwe 01:50 Inflation Consequences 04:48 Writing "End of America" 10:46 Beginnings in Finance 3:02 Adoption Story 17:04 Agora Firing 22:16 Launching Newsletter 24:29 $3 Billion Handshake 28:25 Bill Bonner's Lesson 30:06 Marketwise's 'Ill-Begotten IPO' 34:13 Spooling Up Porter & Co 42:37 $MKTW Stock 44:19 Vision for Marketwise 46:27 Newsletter Business Model 48:40 Wall Street Talk 53:38 Writing Secrets 57:37 Screening Investments 1:03:13 Boston Blackout 1:07:34 History's Biggest Bubble 1:14:39 Finding Safe Haven 1:15:59 Jim Rogers 1:18:10 Commodities Outlook 1:20:58 Idiotic Taxation 1:28:04 Living in Baltimore 1:31:14 Barn-Work 1:32:43

Porter Stansberry turned $36,000 into $3,000,000,000 by writing financial research… after his future business partner fired him... Here's what I learned from Porter Stansberry about writing and selling financial research: 1. Hierarchy of Information Value: In the crowded financial information space, Porter stands out by providing clear, actionable advice, pushing against conventional views. “The news is free because it tells you what happened. Newsletters are expensive because they tell you what to do about it.” 2. Masterful Financial Storytelling: Porter's a master storyteller. He unearths captivating historical tales, dripping with human drama, and seamlessly melds them with modern investment concepts. A voracious reader, Porter often devours a book a day. These stories make his content addictive, easy to grasp, and unforgettable. 3. Creating 'Eureka Moments': Porter leads his readers to conclusions without explicitly stating them. For instance, consider his story about Shelby Davis, who transformed $50,000 into a billion dollars through passive investments in property and casualty insurance stocks. Porter doesn't tell you it's a fantastic business model; he shows you. These "Eureka Moments" are not only enjoyable to read but also encourage independent thinking, fostering loyal readership. 4. In-depth Research: Porter's best investment ideas come from his network of industry specialists he’s in constant contact with. These include a deep talent roster within his companies and a network of external contacts he shares ideas with. Porter strives to cover only topics where he’s had 3-4 conversations with industry insiders, adding depth and credibility to his work. 5. Clear and Simple Writing: To make his ideas more accessible, Porter writes to a novice reading level. He focuses on straightforward, active language (No passive voice), using short, simple sentences. Porter meticulously edits and rewrites. His epic 'End of America' went through 15 versions before selling over 500,000 subscriptions. Porter also prefers clear sentences over excessive visuals. 6. Effective Promotion: Porter’s marketing and research follow the same approach: every sentence engages and adds value. His campaigns tackle big, important issues that affect readers’ lives. Successful campaigns are amplified through collaboration with other publishers. 7. Ethical Standards: For Porter, having a great track record of recommendations is the ultimate skin-in-the-game. Not trading in the securities he covers helps him maintain objectivity, avoid conflicts, and lessen legal risk. 8. Full Tilt Dedication: Porter swears by one way of doing investment research: "Full tilt." His success is a testament to his intense work ethic and decades of relentless trial and error. Don't miss the full interview with Porter Stansberry below, where he delves into his personal journey, the challenges of Marketwise's IPO, and his perspective on current market dynamics. For past stories on Porter, a link to a YouTube interview, and a transcript, check out the replies. Follow me for more insights on finance and investing! TIMESTAMPS: Going Viral in Zimbabwe 01:50 Inflation Consequences 04:48 Writing "End of America" 10:46 Beginnings in Finance 3:02 Adoption Story 17:04 Agora Firing 22:16 Launching Newsletter 24:29 $3 Billion Handshake 28:25 Bill Bonner's Lesson 30:06 Marketwise's 'Ill-Begotten IPO' 34:13 Spooling Up Porter & Co 42:37 $MKTW Stock 44:19 Vision for Marketwise 46:27 Newsletter Business Model 48:40 Wall Street Talk 53:38 Writing Secrets 57:37 Screening Investments 1:03:13 Boston Blackout 1:07:34 History's Biggest Bubble 1:14:39 Finding Safe Haven 1:15:59 Jim Rogers 1:18:10 Commodities Outlook 1:20:58 Idiotic Taxation 1:28:04 Living in Baltimore 1:31:14 Barn-Work 1:32:43

Tommy Humphreys

417,651 views • 2 years ago