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Offense at scale starts with an API. Today, we’re launching the XBOW Public API in Public Preview. Autonomous pentesting gave teams expert-level assessments in hours. But turning that capability into infrastructure required one more piece: programmatic access. Teams can now trigger and manage pentests across their entire portfolio; start,...

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XBOW

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12,257 просмотров • 7 месяцев назад •via X (Twitter)

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XBOW + Microsoft: continuous pentesting, now inside Microsoft Security. 🚀 Discover assets + launch pentests in Security Copilot 🔍 Exploit-validated findings → Sentinel 📐 Copilot correlates attack paths with live telemetry No more periodic tests. No more disconnected reports. Just a continuous loop: test → validate → detect → remediate → retest See how to deploy: Booth #1843 RSAC
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XBOW + Microsoft: continuous pentesting, now inside Microsoft Security. 🚀 Discover assets + launch pentests in Security Copilot 🔍 Exploit-validated findings → Sentinel 📐 Copilot correlates attack paths with live telemetry No more periodic tests. No more disconnected reports. Just a continuous loop: test → validate → detect → remediate → retest See how to deploy: Booth #1843 RSAC

XBOW

83,265 просмотров • 6 месяцев назад

What Tesla is doing with Cybercab shouldn’t be possible even by modern standards....it’s one of the hardest engineering problems any company can attempt The challenge is far bigger than building a car with no steering wheel or pedals The car has to drive itself, operate reliably on public roads, work as part of a massive autonomous fleet, be manufactured at enormous scale and ultimately function as an entire transportation network Tesla is compressing what would normally take decades of engineering into an insanely short timeline AI, autonomy, vehicle design, manufacturing at massive scale, infrastructure, fleet operations....all of it has to work together as one system Most companies would struggle to solve even one of these problems Tesla is solving all of them at the same time and turning the whole thing into something that can scale Cybercab is one of the boldest engineering bets any company has ever made And now Tesla is making it real at scale
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What Tesla is doing with Cybercab shouldn’t be possible even by modern standards....it’s one of the hardest engineering problems any company can attempt The challenge is far bigger than building a car with no steering wheel or pedals The car has to drive itself, operate reliably on public roads, work as part of a massive autonomous fleet, be manufactured at enormous scale and ultimately function as an entire transportation network Tesla is compressing what would normally take decades of engineering into an insanely short timeline AI, autonomy, vehicle design, manufacturing at massive scale, infrastructure, fleet operations....all of it has to work together as one system Most companies would struggle to solve even one of these problems Tesla is solving all of them at the same time and turning the whole thing into something that can scale Cybercab is one of the boldest engineering bets any company has ever made And now Tesla is making it real at scale

X Freeze

12,961 просмотров • 23 дней назад

For the past 2 months, XBOW has been testing Mythos Preview under embargo as part of a select early-access group. Today, we can finally share what we found. The headline: Mythos Preview is a major advance. It is substantially better than prior models at finding vulnerability candidates, especially when source code is available. But it’s not perfect. We surfaced issues with exploit validation, judgment, and efficiency. Our full write-up covers where Mythos Preview shines, where it still needs support, and what we think this means for the future of offensive security:
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For the past 2 months, XBOW has been testing Mythos Preview under embargo as part of a select early-access group. Today, we can finally share what we found. The headline: Mythos Preview is a major advance. It is substantially better than prior models at finding vulnerability candidates, especially when source code is available. But it’s not perfect. We surfaced issues with exploit validation, judgment, and efficiency. Our full write-up covers where Mythos Preview shines, where it still needs support, and what we think this means for the future of offensive security:

XBOW

106,045 просмотров • 4 месяцев назад

Introducing Lobstack: Simplifying AI Deployment and Management For the past month, I’ve been building something I kept running into myself. Everone knows by now that OpenClaw is powerful. BUT, deploying it shouldn’t require DevOps. Lobstack provisions a dedicated server for you and installs OpenClaw automatically — one click, and your agent is live within minutes. No Docker setup. No terminal. No infrastructure babysitting. Not one line of code. You can choose your dedicated server location, select your LLM (our universal API or your own API), and interact with your agent through the custom web UI or Telegram. Discord, Slack, and more coming. Under the hood, it’s persistent, guarded, and capped — built to run 24/7 like real software. We’re also building a skill + API marketplace and exploring desktop and mobile apps to make agent communication seamless. It’s early. I’d genuinely value feedback from builders, operators, and anyone thinking about autonomous AI infrastructure. Or even any business owner that wants to implement AI into their operations. Try it out: Follow Lobstack for updates. Thanks OpenClaw🦞 community and Peter Steinberger 🦞
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Introducing Lobstack: Simplifying AI Deployment and Management For the past month, I’ve been building something I kept running into myself. Everone knows by now that OpenClaw is powerful. BUT, deploying it shouldn’t require DevOps. Lobstack provisions a dedicated server for you and installs OpenClaw automatically — one click, and your agent is live within minutes. No Docker setup. No terminal. No infrastructure babysitting. Not one line of code. You can choose your dedicated server location, select your LLM (our universal API or your own API), and interact with your agent through the custom web UI or Telegram. Discord, Slack, and more coming. Under the hood, it’s persistent, guarded, and capped — built to run 24/7 like real software. We’re also building a skill + API marketplace and exploring desktop and mobile apps to make agent communication seamless. It’s early. I’d genuinely value feedback from builders, operators, and anyone thinking about autonomous AI infrastructure. Or even any business owner that wants to implement AI into their operations. Try it out: Follow Lobstack for updates. Thanks OpenClaw🦞 community and Peter Steinberger 🦞

Brandon M. Crenshaw

128,050 просмотров • 7 месяцев назад

I built a stock market API in 42 days. All during buildspace s5. What is the API? Today, it lets you pull financials for 16,000 tickers going back 30+ years. Why did I build the API? Because the big providers have: • poor API design • poor documentation • expensive sales contracts • strict data retention policies How did I build it? I started building the API from scratch at the start of s5 in early June. I got deep into the SEC Edgar API and cracked how to scrape at scale. Sonnet 3.5 from Anthropic was a godsend for this. I hacked early mornings, late nights, and weekends, with a full-time job. Day by day. Code by code. I set weekly goals and hit each one. End result: I’m excited to see where this API goes. It may go nowhere, but I’ll always know that I can set goals and crush them.
1:29

I built a stock market API in 42 days. All during buildspace s5. What is the API? Today, it lets you pull financials for 16,000 tickers going back 30+ years. Why did I build the API? Because the big providers have: • poor API design • poor documentation • expensive sales contracts • strict data retention policies How did I build it? I started building the API from scratch at the start of s5 in early June. I got deep into the SEC Edgar API and cracked how to scrape at scale. Sonnet 3.5 from Anthropic was a godsend for this. I hacked early mornings, late nights, and weekends, with a full-time job. Day by day. Code by code. I set weekly goals and hit each one. End result: I’m excited to see where this API goes. It may go nowhere, but I’ll always know that I can set goals and crush them.

Virat Singh

236,575 просмотров • 2 лет назад

Proud to announce that I've acquired a stake in ListKit. 10 years ago I was sending cold emails one by one with no data and no infrastructure. Every business I've built since came down to reaching the right people at scale. ListKit is the first platform that gives a first-time founder the same outbound firepower as a funded sales team. So this isn't a passive investment. Their full sending infrastructure is being built directly into Credit to the founders Andre, Daniel, Christian, Dan and Oliver. Bootstrapped to a €45M valuation in under three years. Latest addition to the portfolio. Now back to work.
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Proud to announce that I've acquired a stake in ListKit. 10 years ago I was sending cold emails one by one with no data and no infrastructure. Every business I've built since came down to reaching the right people at scale. ListKit is the first platform that gives a first-time founder the same outbound firepower as a funded sales team. So this isn't a passive investment. Their full sending infrastructure is being built directly into Credit to the founders Andre, Daniel, Christian, Dan and Oliver. Bootstrapped to a €45M valuation in under three years. Latest addition to the portfolio. Now back to work.

Iman Gadzhi

542,980 просмотров • 1 месяц назад

Anthropic won't like this open-source repo. It is going to cost LLM providers a lot of money. Every CI run of an AI app today sends real requests to providers like OpenAI or Anthropic. Like any other LLM call, this too gets billed at actual API rates. So for teams with high commit volumes, this accumulates into a meaningful chunk of API spend. One common hack devs use is that instead of invoking the LLM API, the test calls a fake local server that speaks the same API and returns a dummy response. The catch is that the dummy response is a copy of what the provider returned on the day it was saved, and providers keep adding fields and changing types. So the tests keep passing against a schema that's no longer valid, while the real integration breaks in production. A smart approach is now actually implemented in CopilotKit🪁's recently open-sourced aimock project. Every day, the repo's own CI sends a handful of requests to the real API and the same requests to the fake server, then compares both against the official client library's type definitions. Those are the only real API calls in the whole setup, and they run on the repo's own keys, not in anyone else's CI. A single team can push hundreds of commits a day, and thousands of teams are already doing that with coding agents. All of those runs stay offline, because one repo checks against the real API on everyone's behalf. When a check fails, a coding agent updates aimock's built-in response schema, the full test suite has to pass, and a patch version ships to npm. By simply upgrading the package, the corrected schema gets reflected in every project using it. The capability is not just limited to a single provider. The same server works for Claude, OpenAI, Gemini, Bedrock, Azure, Ollama, plus MCP tools, A2A agents, AG-UI event streams, vector DBs like Pinecone and Qdrant, and search, speech, image, and video endpoints. Here's the repo: (don't forget to star it ⭐) That said, mocking your API calls is one thing. AI engineers should also know how to test agents properly in the first place, which several teams still skip. I wrote a full walkthrough on that, covering build, testing, evals, tracing, and deployment. Read it below.
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Anthropic won't like this open-source repo. It is going to cost LLM providers a lot of money. Every CI run of an AI app today sends real requests to providers like OpenAI or Anthropic. Like any other LLM call, this too gets billed at actual API rates. So for teams with high commit volumes, this accumulates into a meaningful chunk of API spend. One common hack devs use is that instead of invoking the LLM API, the test calls a fake local server that speaks the same API and returns a dummy response. The catch is that the dummy response is a copy of what the provider returned on the day it was saved, and providers keep adding fields and changing types. So the tests keep passing against a schema that's no longer valid, while the real integration breaks in production. A smart approach is now actually implemented in CopilotKit🪁's recently open-sourced aimock project. Every day, the repo's own CI sends a handful of requests to the real API and the same requests to the fake server, then compares both against the official client library's type definitions. Those are the only real API calls in the whole setup, and they run on the repo's own keys, not in anyone else's CI. A single team can push hundreds of commits a day, and thousands of teams are already doing that with coding agents. All of those runs stay offline, because one repo checks against the real API on everyone's behalf. When a check fails, a coding agent updates aimock's built-in response schema, the full test suite has to pass, and a patch version ships to npm. By simply upgrading the package, the corrected schema gets reflected in every project using it. The capability is not just limited to a single provider. The same server works for Claude, OpenAI, Gemini, Bedrock, Azure, Ollama, plus MCP tools, A2A agents, AG-UI event streams, vector DBs like Pinecone and Qdrant, and search, speech, image, and video endpoints. Here's the repo: (don't forget to star it ⭐) That said, mocking your API calls is one thing. AI engineers should also know how to test agents properly in the first place, which several teams still skip. I wrote a full walkthrough on that, covering build, testing, evals, tracing, and deployment. Read it below.

Akshay 🚀

62,821 просмотров • 1 месяц назад

we’re launching Codag (Y Combinator S26) - the compression and control layer for agent tools. we started with logs. then customers pulled us into searches, tests, builds, and api responses. the pattern was always the same: agents burning context on repetitive output, with teams unable to see where the tokens went. Codag now cuts input-token usage ~3x at answer parity and gives teams visibility across their agent stack. agents are becoming the primary users of software - companies are adding agents, models, providers, and MCP servers faster than they can measure or control them. most cannot answer which tools their agents invoke, what those calls cost, or where the waste is. we’re building the layer between them and everything they read. 90+ engineering orgs already use Codag today to reduce that spend and see agent tool usage across provider. find out more and book a demo at
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we’re launching Codag (Y Combinator S26) - the compression and control layer for agent tools. we started with logs. then customers pulled us into searches, tests, builds, and api responses. the pattern was always the same: agents burning context on repetitive output, with teams unable to see where the tokens went. Codag now cuts input-token usage ~3x at answer parity and gives teams visibility across their agent stack. agents are becoming the primary users of software - companies are adding agents, models, providers, and MCP servers faster than they can measure or control them. most cannot answer which tools their agents invoke, what those calls cost, or where the waste is. we’re building the layer between them and everything they read. 90+ engineering orgs already use Codag today to reduce that spend and see agent tool usage across provider. find out more and book a demo at

Michael Zhou

163,206 просмотров • 1 месяц назад

This is a standard practice for almost all Tier-1 banking applications in Nigeria, and for some fintech applications I’ve previously performed pentests on. Client-side encryption isn’t a total waste, or a waste of compute, as some people have claimed, but rather a measure to protect against API tampering or API request/response manipulation between the client and the server when implemented properly. Even with HTTPS, attackers can capture a decrypted version of web or mobile API data in transit because the browser and the server establish a level of trust during the TLS handshake. Attackers can leverage this trust to capture & proxy already-decrypted traffic, tamper with it, and then forward it to the server. This allows them to override what the user interface or client is originally supposed to send and replace it with data of their choosing. That is why validation needs to be performed on both the client and the server side. To wrap up, encrypting API requests and responses makes it significantly harder for attackers to tamper with data, even if they capture the traffic, unless they have access to the encryption details (algorithm, encryption mode, key size, secret key, and initialization vector), assuming asymmetric encryption is used. In the demo below, you can see how I discovered additional parameters (balance, is_admin) in the API response, captured the registration API request, despite it being sent over HTTPS from the interface, added the discovered parameters, and successfully inflated my balance to 50 billion and also escalated my privileges to admin, and ultimately deleted the accounts of two live users/customers. In the second slide, I captured an API traffic of a bank app, and you can see how difficult the payloads are to read.
2:38

This is a standard practice for almost all Tier-1 banking applications in Nigeria, and for some fintech applications I’ve previously performed pentests on. Client-side encryption isn’t a total waste, or a waste of compute, as some people have claimed, but rather a measure to protect against API tampering or API request/response manipulation between the client and the server when implemented properly. Even with HTTPS, attackers can capture a decrypted version of web or mobile API data in transit because the browser and the server establish a level of trust during the TLS handshake. Attackers can leverage this trust to capture & proxy already-decrypted traffic, tamper with it, and then forward it to the server. This allows them to override what the user interface or client is originally supposed to send and replace it with data of their choosing. That is why validation needs to be performed on both the client and the server side. To wrap up, encrypting API requests and responses makes it significantly harder for attackers to tamper with data, even if they capture the traffic, unless they have access to the encryption details (algorithm, encryption mode, key size, secret key, and initialization vector), assuming asymmetric encryption is used. In the demo below, you can see how I discovered additional parameters (balance, is_admin) in the API response, captured the registration API request, despite it being sent over HTTPS from the interface, added the discovered parameters, and successfully inflated my balance to 50 billion and also escalated my privileges to admin, and ultimately deleted the accounts of two live users/customers. In the second slide, I captured an API traffic of a bank app, and you can see how difficult the payloads are to read.

Ghost St Badmus

217,957 просмотров • 9 месяцев назад

Great energy at our HUMAIN Town Hall this week. Our focus is clear: execution. With LEAP ahead of us, the team is moving with urgency to show what HUMAIN is building, what we are delivering today, and the scale of what comes next. We also heard directly from our Core and Compute teams on the progress being made across the business. These are the teams turning ambition into infrastructure, products, partnerships, and real outcomes, and their updates reinforced just how much momentum we are building. One thing I really enjoyed was bringing more of our people onto the stage from across the company. The people doing the work should have the opportunity to tell the story. This is exactly my mentality going into LEAP: more voices, more ownership, and more recognition for the teams executing every day on the ground in Saudi Arabia. Now, all eyes are on #LEAP. We have an opportunity to show the world not just our vision for AI, but what HUMAIN is already delivering. #HUMAIN #TheEndOfLimits
0:44

Great energy at our HUMAIN Town Hall this week. Our focus is clear: execution. With LEAP ahead of us, the team is moving with urgency to show what HUMAIN is building, what we are delivering today, and the scale of what comes next. We also heard directly from our Core and Compute teams on the progress being made across the business. These are the teams turning ambition into infrastructure, products, partnerships, and real outcomes, and their updates reinforced just how much momentum we are building. One thing I really enjoyed was bringing more of our people onto the stage from across the company. The people doing the work should have the opportunity to tell the story. This is exactly my mentality going into LEAP: more voices, more ownership, and more recognition for the teams executing every day on the ground in Saudi Arabia. Now, all eyes are on #LEAP. We have an opportunity to show the world not just our vision for AI, but what HUMAIN is already delivering. #HUMAIN #TheEndOfLimits

Tareq Amin

26,209 просмотров • 1 месяц назад

We knew from day one that unlocking constellation-scale manufacturing would be more than just a hardware challenge. That's why we built Octopus — our proprietary operating system, developed in-house to handle the many complexities of rapidly scaling satellite manufacturing. Octopus provides an end-to-end source of truth, from first customer conversation to orbit, enabling streamlined traceability that ensures speed never comes at the expense of quality. Octopus is built on Palantir's Foundry and AIP platforms, which provide the underlying infrastructure to rapidly prototype and deploy custom applications across the entire company. By building Octopus on Palantir, we have unified scheduling, procurement, receiving, inspection, inventory management, and production into a single integrated workflow — enabling predictable and secure, constellation-scale production. Hear from Apex CEO, Ian Cinnamon, and the rest of the team on how Apex's partnership with Palantir is unlocking rate production at Factory One, and laying the foundation for the next generation of satellite manufacturing.
5:25

We knew from day one that unlocking constellation-scale manufacturing would be more than just a hardware challenge. That's why we built Octopus — our proprietary operating system, developed in-house to handle the many complexities of rapidly scaling satellite manufacturing. Octopus provides an end-to-end source of truth, from first customer conversation to orbit, enabling streamlined traceability that ensures speed never comes at the expense of quality. Octopus is built on Palantir's Foundry and AIP platforms, which provide the underlying infrastructure to rapidly prototype and deploy custom applications across the entire company. By building Octopus on Palantir, we have unified scheduling, procurement, receiving, inspection, inventory management, and production into a single integrated workflow — enabling predictable and secure, constellation-scale production. Hear from Apex CEO, Ian Cinnamon, and the rest of the team on how Apex's partnership with Palantir is unlocking rate production at Factory One, and laying the foundation for the next generation of satellite manufacturing.

Apex - Satellite Platforms 🛰

33,843 просмотров • 6 месяцев назад

Introducing Base44 Superagents. AI agents built with managed infrastructure, secured by default, one-click integrations, and 24/7 execution from the start. Everything is taken care of so you can focus on what your agent does, not how to get it running. That means no API keys to juggle, no config files, no security setup, and no maintenance. We handle all of it. Your Superagent connects to all the tools you already use in one click, runs on schedules and triggers, remembers context across sessions, acts proactively on your behalf, and keeps working around the clock. All from wherever you already are, WhatsApp, Telegram, Slack, or your browser. The AI agent everyone's been waiting for, with everything you need already built in. We're excited to get this into your hands, so we're giving free credits to everyone who comments and reposts in the next 24 hours.
1:51

Introducing Base44 Superagents. AI agents built with managed infrastructure, secured by default, one-click integrations, and 24/7 execution from the start. Everything is taken care of so you can focus on what your agent does, not how to get it running. That means no API keys to juggle, no config files, no security setup, and no maintenance. We handle all of it. Your Superagent connects to all the tools you already use in one click, runs on schedules and triggers, remembers context across sessions, acts proactively on your behalf, and keeps working around the clock. All from wherever you already are, WhatsApp, Telegram, Slack, or your browser. The AI agent everyone's been waiting for, with everything you need already built in. We're excited to get this into your hands, so we're giving free credits to everyone who comments and reposts in the next 24 hours.

Base44

1,583,557 просмотров • 6 месяцев назад

Possibly the biggest change in Microsoft Teams history? Teams without the Teams? Public preview in November to desktop, mobile, iOS, and Android. Here is what you need to know: 🟣 Microsoft is introducing a new combined chat and channels experience. Meaning chat and teams/channels will show up under the "Chat" sidebar icon. No more dedicated Teams sidebar button. 🟣 Teams and Channels will have their own section in chat. 🟣 Group your chats and/or channels however best works for you with favourites and custom sections (up to 50 sections and 50 items per section) 🟣 “Unread,” “Chat,” “Channels,” “Meetings,” and “Muted” Filters 🟣 New message can initiate a new chat or new channel post 🟣 New Mention view gathers all your mentions into a single interactive list. 🟣 At launch, a self-service, guided onboarding will help users discover the new experience and configure it to their preferences (on or off) 🟣 Users who prefer to keep chat and channels separate can easily do so during the onboarding process or at any time later. So far, it looks like there are no IT/tenant-level override controls. This experience change is targeted at getting more users using teams/channels. Most Teams users live in "chats", not teams/channels. There will undoubtedly be some pushback from users who use teams/channels well and like having the mental separation of teams/channels vs chats. I've been fortunate to be using it for a while, and I'm personally a big fan; my use of the correct channels has gone way up, and now I can find them all in one place in the app, particularly on mobile. I'm now much more likely to see channel conversations I need to see. What do you think? #MicrosoftTeams #Microsoft365
0:31

Possibly the biggest change in Microsoft Teams history? Teams without the Teams? Public preview in November to desktop, mobile, iOS, and Android. Here is what you need to know: 🟣 Microsoft is introducing a new combined chat and channels experience. Meaning chat and teams/channels will show up under the "Chat" sidebar icon. No more dedicated Teams sidebar button. 🟣 Teams and Channels will have their own section in chat. 🟣 Group your chats and/or channels however best works for you with favourites and custom sections (up to 50 sections and 50 items per section) 🟣 “Unread,” “Chat,” “Channels,” “Meetings,” and “Muted” Filters 🟣 New message can initiate a new chat or new channel post 🟣 New Mention view gathers all your mentions into a single interactive list. 🟣 At launch, a self-service, guided onboarding will help users discover the new experience and configure it to their preferences (on or off) 🟣 Users who prefer to keep chat and channels separate can easily do so during the onboarding process or at any time later. So far, it looks like there are no IT/tenant-level override controls. This experience change is targeted at getting more users using teams/channels. Most Teams users live in "chats", not teams/channels. There will undoubtedly be some pushback from users who use teams/channels well and like having the mental separation of teams/channels vs chats. I've been fortunate to be using it for a while, and I'm personally a big fan; my use of the correct channels has gone way up, and now I can find them all in one place in the app, particularly on mobile. I'm now much more likely to see channel conversations I need to see. What do you think? #MicrosoftTeams #Microsoft365

Tom Arbuthnot

64,556 просмотров • 1 год назад

Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.
2:00

Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.

King

155,637 просмотров • 9 месяцев назад

I just vibe-coded a Meta Ads Bulk Uploader in Claude Code that uploads dozens of Facebook and Instagram ads in minutes 🤯 Drag and drop your creatives, connect your live Meta account, publish directly through the Marketing API. Built 100% in Claude Code. Perfect for media buyers and DTC brands who are still uploading ads one at a time in Ads Manager. If you're a media buyer spending 10+ hours a week uploading ads one at a time in Ads Manager — attaching videos, picking thumbnails, naming ads, toggling enhancements, repeating 50 times ... This eliminates the entire workflow: → Drag and drop dozens of images/videos at once → Add multiple primary texts, headlines, and descriptions per batch → Pick your campaign and ad set directly from your live Meta account → Clone existing ad sets with one click → Upload custom thumbnails, pick your CTA, launch paused or active No uploading ads one by one. No 2-3 hours of clicking. No $99/month SaaS subscription. What this tool does: → A bulk uploader that publishes directly through Meta's Marketing API → Full creative control — thumbnails, CTAs, ad names, enhancements — all in one shot → Ad set cloning so you can scale winning setups instantly → A free alternative to the $99/month tools that do the exact same thing I'm giving away every prompt I used to build it so you can vibe-code the whole thing yourself. Want all the prompts? > Like this post > Comment "BULK" And I'll send it over (must be following so I can DM)
0:20

I just vibe-coded a Meta Ads Bulk Uploader in Claude Code that uploads dozens of Facebook and Instagram ads in minutes 🤯 Drag and drop your creatives, connect your live Meta account, publish directly through the Marketing API. Built 100% in Claude Code. Perfect for media buyers and DTC brands who are still uploading ads one at a time in Ads Manager. If you're a media buyer spending 10+ hours a week uploading ads one at a time in Ads Manager — attaching videos, picking thumbnails, naming ads, toggling enhancements, repeating 50 times ... This eliminates the entire workflow: → Drag and drop dozens of images/videos at once → Add multiple primary texts, headlines, and descriptions per batch → Pick your campaign and ad set directly from your live Meta account → Clone existing ad sets with one click → Upload custom thumbnails, pick your CTA, launch paused or active No uploading ads one by one. No 2-3 hours of clicking. No $99/month SaaS subscription. What this tool does: → A bulk uploader that publishes directly through Meta's Marketing API → Full creative control — thumbnails, CTAs, ad names, enhancements — all in one shot → Ad set cloning so you can scale winning setups instantly → A free alternative to the $99/month tools that do the exact same thing I'm giving away every prompt I used to build it so you can vibe-code the whole thing yourself. Want all the prompts? > Like this post > Comment "BULK" And I'll send it over (must be following so I can DM)

Mike Futia

42,915 просмотров • 6 месяцев назад

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.
0:24

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

Gipp 🦅

11,334 просмотров • 2 месяцев назад

Every marketing leader I talk to is facing the same challenge: Scale 📶 The demand for content has exploded, but the systems behind it haven’t kept up. Teams are expected to launch more campaigns, in more markets, with more data, all while maintaining brand integrity and speed. Chat-based AI tools help individuals move faster. But they don’t solve the harder problem: scaling content creation across markets and audiences. Over the past year, we’ve seen the limits of chat-based AI inside large organizations. 📢 Today, we’re introducing ⊞ Jasper Grid ⊞ — the backbone of Content Pipelines. ⊞ Grid is a unified, no-code system that lets AI agents plan, produce, and optimize content at scale. It transforms structured inputs like briefs, data, and brand guidelines into launch-ready assets with speed and precision. Under the hood, Grid runs on a network of modular AI agents, a resilient scheduler, and deep integrations with the tools marketers already use — all governed by brand guardrails so teams can move fast without losing control. 🏆 The result: faster time to market, consistent brand execution, and true operational scale. AI is becoming infrastructure. The next generation of enterprise marketing will run on systems built for speed, precision, and trust. Proud of the Jasper team and our early customers who helped shape this moment.
1:15

Every marketing leader I talk to is facing the same challenge: Scale 📶 The demand for content has exploded, but the systems behind it haven’t kept up. Teams are expected to launch more campaigns, in more markets, with more data, all while maintaining brand integrity and speed. Chat-based AI tools help individuals move faster. But they don’t solve the harder problem: scaling content creation across markets and audiences. Over the past year, we’ve seen the limits of chat-based AI inside large organizations. 📢 Today, we’re introducing ⊞ Jasper Grid ⊞ — the backbone of Content Pipelines. ⊞ Grid is a unified, no-code system that lets AI agents plan, produce, and optimize content at scale. It transforms structured inputs like briefs, data, and brand guidelines into launch-ready assets with speed and precision. Under the hood, Grid runs on a network of modular AI agents, a resilient scheduler, and deep integrations with the tools marketers already use — all governed by brand guardrails so teams can move fast without losing control. 🏆 The result: faster time to market, consistent brand execution, and true operational scale. AI is becoming infrastructure. The next generation of enterprise marketing will run on systems built for speed, precision, and trust. Proud of the Jasper team and our early customers who helped shape this moment.

Timothy Young

1,307,286 просмотров • 10 месяцев назад

JENSEN HUANG TOLD STANFORD THAT CLOUD AI WILL NEED 1,000 TIMES MORE ENERGY. THE $249 BOX HE BUILT FOR YOUR DESK USES 25 WATTS AND REPLACES A $200/MONTH AI STACK someone posted a 26 year old imac in airplane mode running a modern ai model with no internet. 1.7 million views in 24 hours. the same thing runs on a $249 box from nvidia the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below
2:09

JENSEN HUANG TOLD STANFORD THAT CLOUD AI WILL NEED 1,000 TIMES MORE ENERGY. THE $249 BOX HE BUILT FOR YOUR DESK USES 25 WATTS AND REPLACES A $200/MONTH AI STACK someone posted a 26 year old imac in airplane mode running a modern ai model with no internet. 1.7 million views in 24 hours. the same thing runs on a $249 box from nvidia the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

15,822 просмотров • 3 месяцев назад

San Francisco is seeing record-low numbers of tents and large vehicles on our streets—and we’re continuing to make progress helping people get off the streets and into stability. We’re now at the lowest level of unsheltered homelessness in 15 years and we have opened the RESET Center, a new tool that allows us to enforce the law while giving people struggling with addiction an alternative to jail and a path to treatment. After one month, the RESET Center has seen more than 500 admissions, with nearly one-third accepting care and treatment. It’s also helping officers get back on the street in under 20 minutes instead of spending hours on the jail booking process. This progress is the result of relentless work by outreach teams, law enforcement officers, and healthcare workers who are helping connect people to services while keeping public spaces safe and clean. We still have more work to do. As we continue reducing tents and large vehicles on our streets, we’ll keep investing in solutions that help people in crisis get the support they need—and make clear that open-air drug use and dealing have no place in San Francisco
0:49

San Francisco is seeing record-low numbers of tents and large vehicles on our streets—and we’re continuing to make progress helping people get off the streets and into stability. We’re now at the lowest level of unsheltered homelessness in 15 years and we have opened the RESET Center, a new tool that allows us to enforce the law while giving people struggling with addiction an alternative to jail and a path to treatment. After one month, the RESET Center has seen more than 500 admissions, with nearly one-third accepting care and treatment. It’s also helping officers get back on the street in under 20 minutes instead of spending hours on the jail booking process. This progress is the result of relentless work by outreach teams, law enforcement officers, and healthcare workers who are helping connect people to services while keeping public spaces safe and clean. We still have more work to do. As we continue reducing tents and large vehicles on our streets, we’ll keep investing in solutions that help people in crisis get the support they need—and make clear that open-air drug use and dealing have no place in San Francisco

Daniel Lurie 丹尼爾·羅偉

17,331 просмотров • 3 месяцев назад

We generated this scene with just one trial on Seedance Enterprise 2.5. We had run this same shot on other models and it took us around 8 attempts before anything came back usable. Generations are billed one at a time so 8 tries to 1 is the entire cost story. 30 seconds, single shot, no stitching, what used to be a day of rerolling is now a single pass, which means the budget goes into testing new concepts instead of rescuing one. Watch the water as the creature moves through it, and watch the camera fall behind during the escape. The realistic physics, smooth camera motion, and shot consistency all came directly from the model.None of that was added in post. We used Seedream 5.0 for the visual ideation and Seedance 2.5 for the video. Both are running on lumina at 4K, up to 50 references to hold a character or product steady across a shot, and region editing to fix one part of a frame without regenerating the whole thing. Seedance 2.5 API is about to launch on BytePlus, and now you can try it directly on Lumina.
0:30

We generated this scene with just one trial on Seedance Enterprise 2.5. We had run this same shot on other models and it took us around 8 attempts before anything came back usable. Generations are billed one at a time so 8 tries to 1 is the entire cost story. 30 seconds, single shot, no stitching, what used to be a day of rerolling is now a single pass, which means the budget goes into testing new concepts instead of rescuing one. Watch the water as the creature moves through it, and watch the camera fall behind during the escape. The realistic physics, smooth camera motion, and shot consistency all came directly from the model.None of that was added in post. We used Seedream 5.0 for the visual ideation and Seedance 2.5 for the video. Both are running on lumina at 4K, up to 50 references to hold a character or product steady across a shot, and region editing to fix one part of a frame without regenerating the whole thing. Seedance 2.5 API is about to launch on BytePlus, and now you can try it directly on Lumina.

Future Stacked

86,430 просмотров • 1 месяц назад