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Postgres 19 can query relationships like a GRAPH NOT with a long manual join chain. With a path: customer → bought → product product ← bought ← similar customer similar customer → follows → brand That is a recommendation system in one readable pattern. SQL/PGQ finds the relationship. COLUMNS...

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alex shapalov

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56,099 просмотров • 2 месяцев назад •via X (Twitter)

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Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.
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Big moment for Postgres! Search has always been Postgres' weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.

Akshay 🚀

215,785 просмотров • 9 месяцев назад

The Meta Creator Marketplace keeps getting better and better. You can now select a Custom or Lookalike audience and see a list of creators with similar audience profiles. This is publicly accessible via the Creator Marketplace API. This means 10 minutes in Claude Code can build a dashboard that constantly uncovers new creators with audience profiles statistically similar to your brand. We used to DREAM OF STUFF LIKE THIS in 2019.
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The Meta Creator Marketplace keeps getting better and better. You can now select a Custom or Lookalike audience and see a list of creators with similar audience profiles. This is publicly accessible via the Creator Marketplace API. This means 10 minutes in Claude Code can build a dashboard that constantly uncovers new creators with audience profiles statistically similar to your brand. We used to DREAM OF STUFF LIKE THIS in 2019.

Peter Czepiga

50,793 просмотров • 4 месяцев назад

grok bot is running 4 agents that generate revenue 24/7 it also built a 3js dashboard for it, with a starship, a dome per business and starman roadstar floating... - finds the prospects out of public data - scores each one before you spend anything - builds the asset before making contact - takes the payment on whop itself - ships the product or deploys the site - shows you what needs approving each morning everything from finding the customer to selling them just wrote a whole guide on how you can set this up exactly:
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grok bot is running 4 agents that generate revenue 24/7 it also built a 3js dashboard for it, with a starship, a dome per business and starman roadstar floating... - finds the prospects out of public data - scores each one before you spend anything - builds the asset before making contact - takes the payment on whop itself - ships the product or deploys the site - shows you what needs approving each morning everything from finding the customer to selling them just wrote a whole guide on how you can set this up exactly:

Chris

56,858 просмотров • 2 месяцев назад

One image. Multiple premium ad concepts. Fotor lets you take a simple product photo and turn it into polished commercial-style content that feels like it belongs in a luxury brand campaign. From extreme close-up details to premium product showcases and cinematic motion shots, you can create completely different visuals from the same reference. What used to require a studio, camera crew, lighting setup, and hours of production can now be done with AI. Product marketing is moving beyond static images. The real game is creating visuals that stop the scroll and make people remember the product.
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One image. Multiple premium ad concepts. Fotor lets you take a simple product photo and turn it into polished commercial-style content that feels like it belongs in a luxury brand campaign. From extreme close-up details to premium product showcases and cinematic motion shots, you can create completely different visuals from the same reference. What used to require a studio, camera crew, lighting setup, and hours of production can now be done with AI. Product marketing is moving beyond static images. The real game is creating visuals that stop the scroll and make people remember the product.

Hope Ai

16,288 просмотров • 2 месяцев назад

Now is the best time for founders and brands to take advantage of this AI bubble to actually stand out from their competitors. First thing that potential customer see is your product video/demo, and If I wanted to buy your product, and the presentation you show me looks crap, I will get an impression you cheaped out on the product as well and just pass. A lot of founders/brands sadly fall for that, especially now with AI hype promising fast and cheap content. Just be honest, if you are a potential customer - would you be converted by AI slop, or videos like ones below, that were fundamentally and manually structured and animated to a T for conversion ?
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Now is the best time for founders and brands to take advantage of this AI bubble to actually stand out from their competitors. First thing that potential customer see is your product video/demo, and If I wanted to buy your product, and the presentation you show me looks crap, I will get an impression you cheaped out on the product as well and just pass. A lot of founders/brands sadly fall for that, especially now with AI hype promising fast and cheap content. Just be honest, if you are a potential customer - would you be converted by AI slop, or videos like ones below, that were fundamentally and manually structured and animated to a T for conversion ?

Leon Fucak

61,980 просмотров • 7 месяцев назад

Telecom giant KKTCELL is deploying DTEC AI Call Center across its customer operations. We're putting our AI-powered enterprise stack in the hands of an industry leader and routing the value created straight back into the ecosystem: 🔹 As with every partnership, 15% of revenue goes directly to $DTEC buybacks. 🔹 Every token bought back moves to the DTEC treasury. Real tech in production. Real enterprise revenue. A stronger treasury. We keep building $DTEC. 🚀
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Telecom giant KKTCELL is deploying DTEC AI Call Center across its customer operations. We're putting our AI-powered enterprise stack in the hands of an industry leader and routing the value created straight back into the ecosystem: 🔹 As with every partnership, 15% of revenue goes directly to $DTEC buybacks. 🔹 Every token bought back moves to the DTEC treasury. Real tech in production. Real enterprise revenue. A stronger treasury. We keep building $DTEC. 🚀

Dtec AI Network

40,403 просмотров • 2 месяцев назад

Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!
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Build AI agents on a time-aware knowledge graph! Utopia is an open-source knowledge system that turns documents, databases, and connected sources into a temporal graph your agents can reason over. Most RAG systems are optimized for one question: what is relevant right now? That works until the underlying knowledge changes. A customer contract gets updated. A project owner changes. A policy is revised. A previous fact may no longer be true, but simply overwriting it means the system loses the history behind that change. Utopia handles this with a bitemporal knowledge graph. Each fact can track both when it was true in the real world and when the system learned about it. When something changes, the old fact is preserved instead of silently disappearing. That means an agent can reason about questions like: • What is true now? • What was true three months ago? • When did this information change? • What evidence was the conclusion based on? The graph is also ontology-aware, so documents are represented as entities, facts, and relationships instead of only chunks and embeddings. That gives the system more structure for reasoning across relationships, resolving entities, detecting conflicting facts, and deriving new information through explicit rules. Key capabilities: • Bitemporal knowledge graph for tracking how facts change over time • Provenance on facts so agents can trace where information came from • Conflict detection instead of silently overwriting contradictory information • Ontology-based reasoning across entities, relationships, and derived facts • Hybrid retrieval across full-text search, vector search, and graph traversal • MCP and agentic RAG support for exposing the knowledge layer directly to agents The interesting part is that this turns the knowledge base into more than a retrieval system. Instead of only finding relevant information, an agent can reason over what changed, what is still valid, how facts are connected, and where each conclusion came from. 100% open source. I've shared the GitHub repo in the comments!

Sumanth

16,653 просмотров • 1 месяц назад

NEW slow-mo vision. NBC clock perfectly synced with the in-stadium clock on WMU’s field goal, down to the frame. If we can find a similar angle like this for the final play, it would completely prove that B1G’s replay system was the one out of sync, not NBC’s.
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NEW slow-mo vision. NBC clock perfectly synced with the in-stadium clock on WMU’s field goal, down to the frame. If we can find a similar angle like this for the final play, it would completely prove that B1G’s replay system was the one out of sync, not NBC’s.

Joel Fritsma ⚡️

167,314 просмотров • 1 месяц назад

Found a site that collects 𝟮𝟯𝟬+ 𝗺𝗼𝘁𝗶𝗼𝗻 𝘃𝗶𝗱𝗲𝗼𝘀 made with Opus 5.5, and every video comes with 𝘁𝗵𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝗼𝗿 𝘀𝗸𝗶𝗹𝗹 𝘁𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝗶𝘁. Each one has a video preview, the full prompt or skill, one-click copy, the author with a link to their original post, and the model used. There are product launch videos, app promos, personal showreels, kinetic type and explainer animations. Pick one you like, paste its prompt into Opus 5.5 and swap in your own product. The whole prompt for the one in the video is 𝗷𝘂𝘀𝘁 𝘁𝘄𝗼 𝘀𝗲𝗻𝘁𝗲𝗻𝗰𝗲𝘀.
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Found a site that collects 𝟮𝟯𝟬+ 𝗺𝗼𝘁𝗶𝗼𝗻 𝘃𝗶𝗱𝗲𝗼𝘀 made with Opus 5.5, and every video comes with 𝘁𝗵𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝗼𝗿 𝘀𝗸𝗶𝗹𝗹 𝘁𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝗶𝘁. Each one has a video preview, the full prompt or skill, one-click copy, the author with a link to their original post, and the model used. There are product launch videos, app promos, personal showreels, kinetic type and explainer animations. Pick one you like, paste its prompt into Opus 5.5 and swap in your own product. The whole prompt for the one in the video is 𝗷𝘂𝘀𝘁 𝘁𝘄𝗼 𝘀𝗲𝗻𝘁𝗲𝗻𝗰𝗲𝘀.

Vox

34,219 просмотров • 15 дней назад

What will it cost to launch a kilogram into space in 2040, if you don't own the rocket? We took a stab at forecasting customer launch price over the next decade. We try very hard to account for everything in a bottom up model. Built off 77k rows of cleaned historical data, more than 500 assumptions and then monte carlo'd 1,999 times. hopefully, we are less wrong then the next guy, but we'll see. Core takeaways: Starship opening to customers and when it does is 'the hinge' in the model. no one else has a viable path to the scale of reusable tonnage to orbit to effect customer prices. Internal usage (like Starlink/Starmind for SpaceX, or similar constellations for others like Terrawave/Blue Origin) dramatically dominates the total upmass to orbit driven by reduced internal costs. Even with the dropping customer prices the difference in $/kg between internal and external uses is dramatic. due to things like payload optimization. Interestingly enough, this model produces a fairly negative case (for customers purchasing launch) in the late 2030s where starship capacity is driven away from customers and toward Moon/Mars and internal endeavors causing a demand shock. You can read about the model, core assumptions and it's build methodology here...
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What will it cost to launch a kilogram into space in 2040, if you don't own the rocket? We took a stab at forecasting customer launch price over the next decade. We try very hard to account for everything in a bottom up model. Built off 77k rows of cleaned historical data, more than 500 assumptions and then monte carlo'd 1,999 times. hopefully, we are less wrong then the next guy, but we'll see. Core takeaways: Starship opening to customers and when it does is 'the hinge' in the model. no one else has a viable path to the scale of reusable tonnage to orbit to effect customer prices. Internal usage (like Starlink/Starmind for SpaceX, or similar constellations for others like Terrawave/Blue Origin) dramatically dominates the total upmass to orbit driven by reduced internal costs. Even with the dropping customer prices the difference in $/kg between internal and external uses is dramatic. due to things like payload optimization. Interestingly enough, this model produces a fairly negative case (for customers purchasing launch) in the late 2030s where starship capacity is driven away from customers and toward Moon/Mars and internal endeavors causing a demand shock. You can read about the model, core assumptions and it's build methodology here...

Aaron Burnett

12,444 просмотров • 1 месяц назад

this is pure f*cking treasure 20 skills for AI agents with 1.99M+ combined stars an agent is not a model. it is a model plus everything you bolt onto it. these are the bolts. five clusters, twenty repos, all open source, all free. ORCHESTRATION · many agents, one plan : > hermes-agent · 247k > AutoGPT · 187k · > MetaGPT · 70k · > autogen · 61k · > crewAI · 59k · RUNTIME / GRAPH · the loop underneath : > langchain · 147k · > deer-flow · 83k · > langgraph · 42k · > deepagents · 30k · > openai-agents-python · 25k · WEB / BROWSER · reach the open internet : > firecrawl · 181k · > browser-use · 115k · > gemini-cli · 107k · MEMORY / RAG · what it keeps between runs : > ragflow · 91k · > mem0 · 66k · > llama_index · 50k · SHIP IT · turn it into a product : > n8n · 188k · > dify · 140k · > TradingAgents · 107k · > adk-python · 22k · most people pick one framework and stop there. that is the mistake. the stack is orchestration on top, a graph runtime under it, browser and memory on the sides, and one deploy layer that makes it a product instead of a demo. every layer above is already written. you are assembling, not inventing. the gap is one weekend wide.
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this is pure f*cking treasure 20 skills for AI agents with 1.99M+ combined stars an agent is not a model. it is a model plus everything you bolt onto it. these are the bolts. five clusters, twenty repos, all open source, all free. ORCHESTRATION · many agents, one plan : > hermes-agent · 247k > AutoGPT · 187k · > MetaGPT · 70k · > autogen · 61k · > crewAI · 59k · RUNTIME / GRAPH · the loop underneath : > langchain · 147k · > deer-flow · 83k · > langgraph · 42k · > deepagents · 30k · > openai-agents-python · 25k · WEB / BROWSER · reach the open internet : > firecrawl · 181k · > browser-use · 115k · > gemini-cli · 107k · MEMORY / RAG · what it keeps between runs : > ragflow · 91k · > mem0 · 66k · > llama_index · 50k · SHIP IT · turn it into a product : > n8n · 188k · > dify · 140k · > TradingAgents · 107k · > adk-python · 22k · most people pick one framework and stop there. that is the mistake. the stack is orchestration on top, a graph runtime under it, browser and memory on the sides, and one deploy layer that makes it a product instead of a demo. every layer above is already written. you are assembling, not inventing. the gap is one weekend wide.

NO1ennn

21,527 просмотров • 1 месяц назад

I just vibe-coded a Shopify Reviews Scraper that swipes all your competitors' customer reviews instantly. 🤯 Paste any competitor's product URL → pull every review in under 30 seconds → export to CSV. Built 100% in Codex. Perfect for brands and agencies who want to mine competitor voice-of-customer data at scale and turn it into winning ad creative. Here's how it works: → Drop a Shopify product URL into the tool → Set your review limit (50, 100, 500+) → Hit scrape — it launches a browser, finds the review widget, pulls every review → Export clean rows: rating, body, author, date → Feed the CSV to Claude and ask "what do customers love and hate about this product" → Turn the output into ad angles, hooks, and headlines No more paying $99/mo for clunky review scraping SaaS that locks you behind credits. What you get: - Any Shopify store's reviews in 30 seconds - Clean structured data (rating, body, author, date) - CSV or JSON export — drops straight into Sheets or an LLM - Unlimited scrapes, no per-review fees - A voice-of-customer firehose you can turn into ad creative on demand This is essentially a competitor review intelligence engine in a box. I'm giving away the full GitHub repo so you can clone it and run it yourself for free. Want the repo? > Like this post > Comment "SCRAPE" And I'll send it over (must be following so I can DM)
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I just vibe-coded a Shopify Reviews Scraper that swipes all your competitors' customer reviews instantly. 🤯 Paste any competitor's product URL → pull every review in under 30 seconds → export to CSV. Built 100% in Codex. Perfect for brands and agencies who want to mine competitor voice-of-customer data at scale and turn it into winning ad creative. Here's how it works: → Drop a Shopify product URL into the tool → Set your review limit (50, 100, 500+) → Hit scrape — it launches a browser, finds the review widget, pulls every review → Export clean rows: rating, body, author, date → Feed the CSV to Claude and ask "what do customers love and hate about this product" → Turn the output into ad angles, hooks, and headlines No more paying $99/mo for clunky review scraping SaaS that locks you behind credits. What you get: - Any Shopify store's reviews in 30 seconds - Clean structured data (rating, body, author, date) - CSV or JSON export — drops straight into Sheets or an LLM - Unlimited scrapes, no per-review fees - A voice-of-customer firehose you can turn into ad creative on demand This is essentially a competitor review intelligence engine in a box. I'm giving away the full GitHub repo so you can clone it and run it yourself for free. Want the repo? > Like this post > Comment "SCRAPE" And I'll send it over (must be following so I can DM)

Mike Futia

29,996 просмотров • 5 месяцев назад

you can legally steal any viral ugc, rewrite it for your product, and generate it with AI UGC. people are generating photorealistic AI actors that hold real products in their hands and reading scripts that convert like real UGC. Vodafone already ran a full TikTok campaign with AI influencers. same engagement, same conversions, fraction of the cost here's the ugc system anyone can run today: - find a product that hits an emotional pain point (aging skin, insecurity, frustration). one retinol serum video has 8M views and 300k likes with comments begging for the link - download the viral video, transcribe it with Gemini, then feed the transcript + your product + your audience into Claude. it spits out 3 scripts that sound like a friend talking, not an ad - build your actor in any ai ugc platform: filter by age, location, accessories, or generate one from a prompt. nano banana places your exact product in her hand with real lighting and shadows - create two versions of the same actor. no product for the problem hook, product in hand for the solution. that switch is what makes it feel like a real story - use the scenes feature to generate actual application footage: fingertips dabbing serum, circular motions, natural hand movement. not just a talking head - a UGC creator charges $200 to $600 for this exact video. it takes 2 minutes and you can make 10 variations tomorrow the brands winning right now aren't the ones with the biggest budgets. they're the ones testing fastest. reply "ugc" + RT and i'll send you the full video so you can build this too.
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you can legally steal any viral ugc, rewrite it for your product, and generate it with AI UGC. people are generating photorealistic AI actors that hold real products in their hands and reading scripts that convert like real UGC. Vodafone already ran a full TikTok campaign with AI influencers. same engagement, same conversions, fraction of the cost here's the ugc system anyone can run today: - find a product that hits an emotional pain point (aging skin, insecurity, frustration). one retinol serum video has 8M views and 300k likes with comments begging for the link - download the viral video, transcribe it with Gemini, then feed the transcript + your product + your audience into Claude. it spits out 3 scripts that sound like a friend talking, not an ad - build your actor in any ai ugc platform: filter by age, location, accessories, or generate one from a prompt. nano banana places your exact product in her hand with real lighting and shadows - create two versions of the same actor. no product for the problem hook, product in hand for the solution. that switch is what makes it feel like a real story - use the scenes feature to generate actual application footage: fingertips dabbing serum, circular motions, natural hand movement. not just a talking head - a UGC creator charges $200 to $600 for this exact video. it takes 2 minutes and you can make 10 variations tomorrow the brands winning right now aren't the ones with the biggest budgets. they're the ones testing fastest. reply "ugc" + RT and i'll send you the full video so you can build this too.

Sulfur

17,708 просмотров • 3 месяцев назад

TIP: TRY AGAIN: FATIGUE EDITION Waitress worked a full shift just to get tipped like it was 1998 Is a $3 tip on $55 disrespectful or just cheap? Would YOU say something if you were the waitress? A waitress hustles all night…refills drinks before they even ask, brings extra ranch like a hero, checks in with perfect timing… Bill comes out: $55. Customer grins like she just won the lottery…leaves $3. THREE. Not even enough for a decent gas station snack. Customer is smiling. Waitress is blinking like her soul just lagged. She picks up the receipt thinking maybe it’s a joke…nah…that’s the full tip. No sequel. No bonus scene. If service was good, what’s the minimum acceptable tip?
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TIP: TRY AGAIN: FATIGUE EDITION Waitress worked a full shift just to get tipped like it was 1998 Is a $3 tip on $55 disrespectful or just cheap? Would YOU say something if you were the waitress? A waitress hustles all night…refills drinks before they even ask, brings extra ranch like a hero, checks in with perfect timing… Bill comes out: $55. Customer grins like she just won the lottery…leaves $3. THREE. Not even enough for a decent gas station snack. Customer is smiling. Waitress is blinking like her soul just lagged. She picks up the receipt thinking maybe it’s a joke…nah…that’s the full tip. No sequel. No bonus scene. If service was good, what’s the minimum acceptable tip?

𝐌𝐑. 𝐖𝐇𝐈𝐓𝐄 ™

39,809 просмотров • 7 месяцев назад

Rivian has announced in a new blog post that it is still aiming to release point-to-point assisted driving later this year, with widespread rollout in 2027. "Enter an address and supervise while your vehicle follows the route from start to finish, handling city streets, highway interchanges and everything in between. A limited rollout begins later this year, with a widespread customer rollout targeted for 2027. The path from Universal Hands-Free to Point-to-Point is shorter than it looks from the outside, because so much of the work is already done. Every mile our fleet drives makes our Large Driving Model smarter for the next one. That's the engine driving R2 autonomy: not just a sensor suite, but a constantly learning fleet powering a vehicle that keeps evolving right in your driveway."
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Rivian has announced in a new blog post that it is still aiming to release point-to-point assisted driving later this year, with widespread rollout in 2027. "Enter an address and supervise while your vehicle follows the route from start to finish, handling city streets, highway interchanges and everything in between. A limited rollout begins later this year, with a widespread customer rollout targeted for 2027. The path from Universal Hands-Free to Point-to-Point is shorter than it looks from the outside, because so much of the work is already done. Every mile our fleet drives makes our Large Driving Model smarter for the next one. That's the engine driving R2 autonomy: not just a sensor suite, but a constantly learning fleet powering a vehicle that keeps evolving right in your driveway."

Sawyer Merritt

176,733 просмотров • 2 месяцев назад

this is pure f*cking treasure these 10 agent skills have 3.49M combined downloads and form a complete working stack planning. react performance. databases. ideation. interface systems. branding. visual polish. search. browser work. production UI 01 grill-with-docs ▸ 02 vercel-react-best-practices ▸ 03 supabase-postgres-best-practices ▸ 04 brainstorming ▸ 05 ui-ux-pro-max ▸ 06 brandkit ▸ 07 impeccable ▸ 08 seo-audit ▸ 09 browser-use ▸ 10 shadcn ▸ the stack covers the full product loop: pressure-test the plan ⮕ shape the system ⮕ build the interface ⮕ harden the database ⮕ polish the experience ⮕ audit discovery ⮕ run the workflow in a real browser save this, then select your gpt-6 astra use case ⭣
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this is pure f*cking treasure these 10 agent skills have 3.49M combined downloads and form a complete working stack planning. react performance. databases. ideation. interface systems. branding. visual polish. search. browser work. production UI 01 grill-with-docs ▸ 02 vercel-react-best-practices ▸ 03 supabase-postgres-best-practices ▸ 04 brainstorming ▸ 05 ui-ux-pro-max ▸ 06 brandkit ▸ 07 impeccable ▸ 08 seo-audit ▸ 09 browser-use ▸ 10 shadcn ▸ the stack covers the full product loop: pressure-test the plan ⮕ shape the system ⮕ build the interface ⮕ harden the database ⮕ polish the experience ⮕ audit discovery ⮕ run the workflow in a real browser save this, then select your gpt-6 astra use case ⭣

beamnxw ./

79,465 просмотров • 1 месяц назад

this brazilian ai tiktok shop creator is quietly printing with one repeatable ugc format same face same store background same casual product demo style ai swaps the product in her hands keeps the lighting, framing, and pacing identical and posts daily across dozens of offers no filming days no brand negotiations no creator management it looks like one real influencer but it’s actually a content engine whoever copies this in first world markets early is going to push past $300k–$500k/month fast rt + comment “shop” and i’ll dm the system (follow for dm)
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this brazilian ai tiktok shop creator is quietly printing with one repeatable ugc format same face same store background same casual product demo style ai swaps the product in her hands keeps the lighting, framing, and pacing identical and posts daily across dozens of offers no filming days no brand negotiations no creator management it looks like one real influencer but it’s actually a content engine whoever copies this in first world markets early is going to push past $300k–$500k/month fast rt + comment “shop” and i’ll dm the system (follow for dm)

Alessandro

90,567 просмотров • 8 месяцев назад

Claude Code + Nano Banana 2 is f*cking cracked for advertorials 🤯 One prompt → a complete presell page with editorial copy, AI product photography, testimonials, and pricing — ready to paste into Shopify. Built 100% in Claude Code. Perfect for DTC brands and agencies running advertorials on Meta who need 5-10 different pages per month but can't keep paying $1,500 each. If you're briefing copywriters, waiting days for a draft, giving notes, waiting again, and still only getting one or two new pages per month... This system eliminates the entire loop: → Enter your brand, product, target customer, and unique mechanism → Pick a style preset (clinical editorial, news exposé, lifestyle magazine, warm and trustworthy) → Claude writes the full page — urgency banner to guarantee to final CTA → Nano Banana 2 generates product images and mechanism diagrams inline → Get back a complete HTML page following the same DR structure that's already scaling on Meta No copywriter back-and-forth. No designing from scratch. No starting from a blank page every time. What you get: → A production-ready HTML advertorial page you paste into Shopify → DR copy structure extracted from real pages scaling on Meta right now → AI-generated product photography and diagrams matched to your brand → 4 style presets that shift tone, colors, and authority framing per niche → A fully customizable system prompt — swap in your own templates and it follows those instead I built 3 complete advertorial pages for 3 different brands in under 5 minutes. Skincare, supplements, and pet products. All different styles, all production-ready. I put together a full playbook with the exact system prompt so you can get this running yourself. Want access for free? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)
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Claude Code + Nano Banana 2 is f*cking cracked for advertorials 🤯 One prompt → a complete presell page with editorial copy, AI product photography, testimonials, and pricing — ready to paste into Shopify. Built 100% in Claude Code. Perfect for DTC brands and agencies running advertorials on Meta who need 5-10 different pages per month but can't keep paying $1,500 each. If you're briefing copywriters, waiting days for a draft, giving notes, waiting again, and still only getting one or two new pages per month... This system eliminates the entire loop: → Enter your brand, product, target customer, and unique mechanism → Pick a style preset (clinical editorial, news exposé, lifestyle magazine, warm and trustworthy) → Claude writes the full page — urgency banner to guarantee to final CTA → Nano Banana 2 generates product images and mechanism diagrams inline → Get back a complete HTML page following the same DR structure that's already scaling on Meta No copywriter back-and-forth. No designing from scratch. No starting from a blank page every time. What you get: → A production-ready HTML advertorial page you paste into Shopify → DR copy structure extracted from real pages scaling on Meta right now → AI-generated product photography and diagrams matched to your brand → 4 style presets that shift tone, colors, and authority framing per niche → A fully customizable system prompt — swap in your own templates and it follows those instead I built 3 complete advertorial pages for 3 different brands in under 5 minutes. Skincare, supplements, and pet products. All different styles, all production-ready. I put together a full playbook with the exact system prompt so you can get this running yourself. Want access for free? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

Mike Futia

34,457 просмотров • 7 месяцев назад

girl humanoids are one dock away from brand deals ngl the question is not “can a girl humanoid make UGC” it is “how much of the creator is still a human on the other side of the dock” → what “forcing” her to shoot actually looks like right now the honest stack is not a free soul with a ring light it is cameras already on the face a soft body that can hold a bottle walk a kitchen and hit a mark plus teleop when the take needs a human wrist behind the curtain mount a phone script the shelf have her pick the product face the light pour the drink set it down you get lifestyle UGC without booking a girl for four hours and a parking spot neo-class home units already see the room through stereo eyes they already learn chores as motion UGC is just choreography with a caption and a brand brief on top → what she can do for real unboxings that stay in frame skincare shelves coffee counters laundry folds “day in my life” walks through a staged apartment hands-on demos where IP68 fingers rinse a glass and the soft suit sells the vibe what she cannot do yet without you taste timing jokes knowing which take feels expensive catching the weird brand-safety face mid-sentence true creator taste still lives in teleop chat and the edit → the quiet part brands care about nobody booked talent nobody flew a creator the ad still can ship if the dock operator hits the brief disclosure still matters “robot / AI-assisted” is not optional forever once the comments clock the gait battery is a shoot day clock 2 to 4 hours soft home duty then she is back on the charger like any tired creator soft suit still photographs like a girl the dock still photographs like the real producer → my take yes you can make her shoot UGC not by yelling “be viral” by treating her like a camera body with legs a script and a remote soul one dock one brief one soft walk through the kitchen and suddenly the brand deal does not need another booking link the creator economy did not die it just got a body that charges overnight so back
0:10

girl humanoids are one dock away from brand deals ngl the question is not “can a girl humanoid make UGC” it is “how much of the creator is still a human on the other side of the dock” → what “forcing” her to shoot actually looks like right now the honest stack is not a free soul with a ring light it is cameras already on the face a soft body that can hold a bottle walk a kitchen and hit a mark plus teleop when the take needs a human wrist behind the curtain mount a phone script the shelf have her pick the product face the light pour the drink set it down you get lifestyle UGC without booking a girl for four hours and a parking spot neo-class home units already see the room through stereo eyes they already learn chores as motion UGC is just choreography with a caption and a brand brief on top → what she can do for real unboxings that stay in frame skincare shelves coffee counters laundry folds “day in my life” walks through a staged apartment hands-on demos where IP68 fingers rinse a glass and the soft suit sells the vibe what she cannot do yet without you taste timing jokes knowing which take feels expensive catching the weird brand-safety face mid-sentence true creator taste still lives in teleop chat and the edit → the quiet part brands care about nobody booked talent nobody flew a creator the ad still can ship if the dock operator hits the brief disclosure still matters “robot / AI-assisted” is not optional forever once the comments clock the gait battery is a shoot day clock 2 to 4 hours soft home duty then she is back on the charger like any tired creator soft suit still photographs like a girl the dock still photographs like the real producer → my take yes you can make her shoot UGC not by yelling “be viral” by treating her like a camera body with legs a script and a remote soul one dock one brief one soft walk through the kitchen and suddenly the brand deal does not need another booking link the creator economy did not die it just got a body that charges overnight so back

Luella

17,610 просмотров • 1 месяц назад