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WebRecon from @D4rk_Intel is another OSINT multi-tool worth knowing about. 🧠 It automates web crawling, tech stack detection, DNS & WHOIS intel, email harvesting, Wayback lookups, and exports clean JSON reports. Try it here:

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31,646 просмотров • 9 месяцев назад •via X (Twitter)

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Super excited to announce the release of #WebRecon - an advanced web recon tool. This tool automates: - Web crawling & data extraction - Technology stack detection - Email harvesting - DNS intelligence & WHOIS lookup - Historical web analysis #Cybersecurity #OSINT

Super excited to announce the release of #WebRecon - an advanced web recon tool. This tool automates: - Web crawling & data extraction - Technology stack detection - Email harvesting - DNS intelligence & WHOIS lookup - Historical web analysis #Cybersecurity #OSINT

D4rk_Intel

37,758 просмотров • 10 месяцев назад

I'm building FRIDAY in public. Friday can now tell me about the latest news in the world. Here's the tech stack: > Sarvam for multi-lingual SST and TTS (free plan) > Gemini through Groq API for core model (free plan) > FastMCP for building tools | Connect it with Groq > Cloudflared for temporary hosting to web > Uses 5 international news outlet's RSS feed as a tool. > Another tool is url opener. Model opens the url through this tool. Making repo public after a few days here on X!

I'm building FRIDAY in public. Friday can now tell me about the latest news in the world. Here's the tech stack: > Sarvam for multi-lingual SST and TTS (free plan) > Gemini through Groq API for core model (free plan) > FastMCP for building tools | Connect it with Groq > Cloudflared for temporary hosting to web > Uses 5 international news outlet's RSS feed as a tool. > Another tool is url opener. Model opens the url through this tool. Making repo public after a few days here on X!

Sagar Tamang

10,887 просмотров • 5 месяцев назад

🔍 OSINT TOOL OF THE DAY Search Inside Messengers Ever wanted to uncover groups, channels, or hidden communities inside messaging apps? Waybien lets you search across major messengers & social platforms using keywords, then filter results with precision: 🌍 Geography 🗣 ️ Language 📌 Topic 👥 Member count Perfect for investigations, intel gathering, and mapping how conversations spread across regions. A clean, efficient, and underrated addition to any OSINT workflow 😄🧠 And in a future post, I’ll share a free tool that lets you map mobile network coverage and locate 2G–5G cell towers. 😉 You can check it out here ------------ P.S. ♻️ Repost if you found this helpful. If you would like to learn more about website OSINT and much more, check out the OSINT Mastery course at you’ll learn OSINT from scratch with a real-life investigation.

🔍 OSINT TOOL OF THE DAY Search Inside Messengers Ever wanted to uncover groups, channels, or hidden communities inside messaging apps? Waybien lets you search across major messengers & social platforms using keywords, then filter results with precision: 🌍 Geography 🗣 ️ Language 📌 Topic 👥 Member count Perfect for investigations, intel gathering, and mapping how conversations spread across regions. A clean, efficient, and underrated addition to any OSINT workflow 😄🧠 And in a future post, I’ll share a free tool that lets you map mobile network coverage and locate 2G–5G cell towers. 😉 You can check it out here ------------ P.S. ♻️ Repost if you found this helpful. If you would like to learn more about website OSINT and much more, check out the OSINT Mastery course at you’ll learn OSINT from scratch with a real-life investigation.

CyberSudo

20,767 просмотров • 8 месяцев назад

🧠 OSINT Tool: Turn Threat Intelligence Into Instant Reports Most analysts monitor forums manually… but automation can surface leaks before you even start searching ⚡ When I’m doing OSINT or threat profiling, one of my favorite approaches is using platforms that visualize threat activity instead of just listing data 🌍 This tool shows a live global map of leaks and underground discussions in real time, which makes spotting patterns and hotspots much faster than scrolling forums manually 👀 It lets you: 📍 Track leak geography visually 🗂 Aggregate data from 300K+ forum logs 🤖 Generate AI-powered OSINT reports automatically For example, during a recon scenario, I needed to understand whether a target appeared in underground chatter. Instead of browsing multiple forums one by one, I used this platform and immediately saw posts linked to their domain, which gave me investigation leads within minutes. This is the kind of tool that saves hours and gives analysts a strategic advantage fast. Tool link: __________ P.S. ♻️ Repost if you found this helpful. If you liked this post and would like to learn more methods and techniques to discover information about people, check out my OSINT Mastery course

🧠 OSINT Tool: Turn Threat Intelligence Into Instant Reports Most analysts monitor forums manually… but automation can surface leaks before you even start searching ⚡ When I’m doing OSINT or threat profiling, one of my favorite approaches is using platforms that visualize threat activity instead of just listing data 🌍 This tool shows a live global map of leaks and underground discussions in real time, which makes spotting patterns and hotspots much faster than scrolling forums manually 👀 It lets you: 📍 Track leak geography visually 🗂 Aggregate data from 300K+ forum logs 🤖 Generate AI-powered OSINT reports automatically For example, during a recon scenario, I needed to understand whether a target appeared in underground chatter. Instead of browsing multiple forums one by one, I used this platform and immediately saw posts linked to their domain, which gave me investigation leads within minutes. This is the kind of tool that saves hours and gives analysts a strategic advantage fast. Tool link: __________ P.S. ♻️ Repost if you found this helpful. If you liked this post and would like to learn more methods and techniques to discover information about people, check out my OSINT Mastery course

CyberSudo

23,522 просмотров • 5 месяцев назад

.Michael Nielsen provides another example of how the tech tree is far vaster than we realize, and how we won't really get to explore much of it at all, even over millions of years: Think about how many interesting biological technologies are downstream of the particular way in which life evolved here on Earth. It's like we're learning about an alien civilization's tech stack. There’s an interesting implication here for thinking about the far future. There could potentially be huge gains from trade between different civilisations, if each one has radically different kinds of technology from the others. Different civilizations might have strong incentives to cooperate and trade.

.Michael Nielsen provides another example of how the tech tree is far vaster than we realize, and how we won't really get to explore much of it at all, even over millions of years: Think about how many interesting biological technologies are downstream of the particular way in which life evolved here on Earth. It's like we're learning about an alien civilization's tech stack. There’s an interesting implication here for thinking about the far future. There could potentially be huge gains from trade between different civilisations, if each one has radically different kinds of technology from the others. Different civilizations might have strong incentives to cooperate and trade.

Dwarkesh Patel

81,321 просмотров • 4 месяцев назад

🚨 Tencent just dropped a serious open-source AI contender. Meet Hy4 Preview built for productivity, coding & agentic workflows. 770B total parameters. 49B active. 1M+ token context. But the numbers aren’t the most interesting part. Hy4 Preview is designed to handle what actually matters: planning, tool use, coding, research, document workflows and complex multi-step tasks. In Tencent’s internal blind evaluation across 203 engineering tasks, it scored 2.99/4, edging out GLM 5.3 (2.92) and Kimi K3 (2.94). From full-stack development and research to workplace automation and even game development, this looks less like “just another LLM” and more like a model built to get real work done. Worth watching 👀 🔗 Try it with WorkBuddy: Tencent Hy Tencent AI WorkBuddy

🚨 Tencent just dropped a serious open-source AI contender. Meet Hy4 Preview built for productivity, coding & agentic workflows. 770B total parameters. 49B active. 1M+ token context. But the numbers aren’t the most interesting part. Hy4 Preview is designed to handle what actually matters: planning, tool use, coding, research, document workflows and complex multi-step tasks. In Tencent’s internal blind evaluation across 203 engineering tasks, it scored 2.99/4, edging out GLM 5.3 (2.92) and Kimi K3 (2.94). From full-stack development and research to workplace automation and even game development, this looks less like “just another LLM” and more like a model built to get real work done. Worth watching 👀 🔗 Try it with WorkBuddy: Tencent Hy Tencent AI WorkBuddy

RAVI KUMAR SAHU

63,568 просмотров • 15 дней назад

APIs are a bottleneck, not a solution! AI agents can't access 95% of the web because most websites simply don't have APIs. Supplier portals, appointment systems, regional job boards, etc., none of them have developer APIs. The valuable data lives behind logins, multi-step forms, and interfaces built for humans, not machines. That's why, for 25 years, users have been stuck with: - Search engines that index 5% of the web (nothing behind authentication) - Manual data entry or fragile scrapers that break with every CSS update That's the problem TinyFish's Mino solves. It is a web automation API that can simultaneously navigate 100s of websites and turn them into structured data. You send it URLs and a goal in plain English. It returns JSON. The approach is different from typical AI agents: Most browser agents use vision models, which screenshot the page, reason about it, decide what to click, screenshot again, and repeat. Every action needs a model call. It's slow and expensive. Mino uses AI to learn the website structure once, then executes it through deterministic code. First run figures out the site. Every run after that is code-level precision in milliseconds. It provides three core capabilities: → Navigate - Handles logins, forms, and multi-step workflows → Extract - Pulls structured JSON from any site layout → Execute - Runs 100+ sites in parallel with stealth mode The performance is production-grade: - 85-95% success rate on complex workflows - 10-30 seconds per task - Pennies per run Moreover, it works on authenticated sites, bypasses anti-bot protections, and returns clean JSON every time. Lastly, you can also integrate its MCP server with clients like Claude Desktop. I have recorded a walkthrough in the video below. Try now, link in the next tweet.

APIs are a bottleneck, not a solution! AI agents can't access 95% of the web because most websites simply don't have APIs. Supplier portals, appointment systems, regional job boards, etc., none of them have developer APIs. The valuable data lives behind logins, multi-step forms, and interfaces built for humans, not machines. That's why, for 25 years, users have been stuck with: - Search engines that index 5% of the web (nothing behind authentication) - Manual data entry or fragile scrapers that break with every CSS update That's the problem TinyFish's Mino solves. It is a web automation API that can simultaneously navigate 100s of websites and turn them into structured data. You send it URLs and a goal in plain English. It returns JSON. The approach is different from typical AI agents: Most browser agents use vision models, which screenshot the page, reason about it, decide what to click, screenshot again, and repeat. Every action needs a model call. It's slow and expensive. Mino uses AI to learn the website structure once, then executes it through deterministic code. First run figures out the site. Every run after that is code-level precision in milliseconds. It provides three core capabilities: → Navigate - Handles logins, forms, and multi-step workflows → Extract - Pulls structured JSON from any site layout → Execute - Runs 100+ sites in parallel with stealth mode The performance is production-grade: - 85-95% success rate on complex workflows - 10-30 seconds per task - Pennies per run Moreover, it works on authenticated sites, bypasses anti-bot protections, and returns clean JSON every time. Lastly, you can also integrate its MCP server with clients like Claude Desktop. I have recorded a walkthrough in the video below. Try now, link in the next tweet.

Akshay 🚀

62,659 просмотров • 8 месяцев назад

The money printer is live 🤯 Take old webpages → Make them modern ✨ Still work in progress, but my inbox is exploding for people who wants to try this. 🔥 This was made for the Norwegian market, so still some sentences that i need to clean up, but here it is. ◆ Input URL of a website ◆ Scrape the site and make a schema from AI ◆ Have a template to populate a siteConfig Gonna push this trough the weekend, if you want to contribute come and join ✨ Repo is 100% Free and Open Source Tech Stack: shadcn Vercel GitHub Prisma Postgres Better Auth #opensource #buildinpublic

The money printer is live 🤯 Take old webpages → Make them modern ✨ Still work in progress, but my inbox is exploding for people who wants to try this. 🔥 This was made for the Norwegian market, so still some sentences that i need to clean up, but here it is. ◆ Input URL of a website ◆ Scrape the site and make a schema from AI ◆ Have a template to populate a siteConfig Gonna push this trough the weekend, if you want to contribute come and join ✨ Repo is 100% Free and Open Source Tech Stack: shadcn Vercel GitHub Prisma Postgres Better Auth #opensource #buildinpublic

CodeHagen

23,469 просмотров • 1 год назад

If you're managing your own retirement money, silver is worth watching even if you never buy a single ounce. How silver moves tells you a lot about where inflation is heading and where institutional money is rotating. Here are 3 things driving silver right now and why they matter for your portfolio: The silver-to-S&P 500 ratio When silver is cheap compared to stocks, a multi-year rally has historically followed. Right now it's near its lowest reading in 50 years. The supply deficit The world has used more silver than it produced for 6 straight years while demand from solar, EVs, and AI keeps growing. Rule changes Basel 3 is forcing banks out of paper silver. China just restricted exports. These shift the floor under prices. Knowing the signals is step one. Knowing what to do with them is step two. I break that down in this thread:

If you're managing your own retirement money, silver is worth watching even if you never buy a single ounce. How silver moves tells you a lot about where inflation is heading and where institutional money is rotating. Here are 3 things driving silver right now and why they matter for your portfolio: The silver-to-S&P 500 ratio When silver is cheap compared to stocks, a multi-year rally has historically followed. Right now it's near its lowest reading in 50 years. The supply deficit The world has used more silver than it produced for 6 straight years while demand from solar, EVs, and AI keeps growing. Rule changes Basel 3 is forcing banks out of paper silver. China just restricted exports. These shift the floor under prices. Knowing the signals is step one. Knowing what to do with them is step two. I break that down in this thread:

Felix Prehn 🐶

36,225 просмотров • 4 месяцев назад

How to monitor the web and research what comes back in 300 lines of code. I want to buy some cheap products on Amazon, but I want to grab them when prices drop. I'm using Parallel Parallel Web Systems here, which is a really easy way to ground your agentic code. Here is how this works: 1. Set up a monitoring event with Parallel for the products 2. If there's a hit, I get a webhook event with the details 3. My script immediately kicks off a Deep Research task 4. The final result tells me whether the product is worth buying I probably spent 1 hour building all of this with Claude Code + the Parallel Agent Skills. I'm linking to the GitHub repo below. Just think about what you could do with this. Here is an easy example (I also set this up): • Monitor news for public tech companies • Analyze the news + price • Assess whether it's worth investing You can do this, put it on autopilot, and get an email anytime there's something worth your time. Here is the video I recorded.

How to monitor the web and research what comes back in 300 lines of code. I want to buy some cheap products on Amazon, but I want to grab them when prices drop. I'm using Parallel Parallel Web Systems here, which is a really easy way to ground your agentic code. Here is how this works: 1. Set up a monitoring event with Parallel for the products 2. If there's a hit, I get a webhook event with the details 3. My script immediately kicks off a Deep Research task 4. The final result tells me whether the product is worth buying I probably spent 1 hour building all of this with Claude Code + the Parallel Agent Skills. I'm linking to the GitHub repo below. Just think about what you could do with this. Here is an easy example (I also set this up): • Monitor news for public tech companies • Analyze the news + price • Assess whether it's worth investing You can do this, put it on autopilot, and get an email anytime there's something worth your time. Here is the video I recorded.

Santiago

14,692 просмотров • 1 месяц назад

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

135,254 просмотров • 5 месяцев назад

Diabetes is something you can read about anywhere. It feels different when it concerns your own family. For me, it started with my Family When the Ritual testnet launched, I wanted to build something with it that had a real use case behind it. So I started working on DiaRoutine: a Telegram bot diabetes diary for my mom. She had tried popular diabetes apps before, but never got used to them. Too many screens, too many buttons, too much friction. Telegram was easier. She could just write what happened during the day, and the bot would structure the entry. At first, it was a small idea. Then I showed the bot to someone close to me who has type 1 diabetes. I watched what confused him, what helped, and what was missing. I also spoke with an endocrinologist at the hospital. He explained that statistics over roughly three months can be important for tuning coefficients and understanding dosage patterns. The problem is that many people do not collect enough data. Sometimes it is laziness. Sometimes it is limited access to apps, paid subscriptions, sanctions, or tracking only part of the picture. That changed how I looked at the product. DiaRoutine is now built to help people collect a fuller diabetes diary with less friction. It can track: - glucose - food and nutrition - insulin - activity - boluses, coefficients, and basal profile - daily and weekly reports - doctor-ready exports - reminders from the agent - pattern observations - Ritual proofs for diary entries, reports, and exports - Raw medical data does not go onchain. Ritual is used as a proof layer and an agent layer. The bot can save hashes and proofs for daily diaries, reports, exports, or individual entries. Glucose, food, insulin, activity, notes, and Telegram ID stay private. The bot also has an AI agent connected to Ritual. It can remind the user to log glucose, follow up after food or activity, prepare a daily overview, and suggest saving a proof in Ritual. It does not give medical advice, change dosages, or send anything to Ritual without confirmation. That is what I find interesting about Ritual here. It is not about putting private medical data onchain. It is about giving an agent a verifiable layer for actions and proofs, while the sensitive data stays private. The user does not need a wallet either. A service wallet pays for gas, so the experience stays simple. DiaRoutine is still in alpha. There are bugs, rough edges, and many things to improve. But the first feedback from people using it has been positive. That matters to me because this was never just a technical experiment. It came from a real family need. Big thanks to Meison (❖,❖) for helping with development and pushing this forward with me. We will keep improving DiaRoutine, testing it with real users, and listening to people who live with diabetes every day. It is still early. But it already feels useful. try @DiaRoutine_Bot in TELEGRAM #BUILDONRITUAL

Annae.nad

12,253 просмотров • 4 месяцев назад

911. Dome. COMMS. MI intel: 2 mysterious rainbows appeared over NYC, where the cabal took down the towers. Rainbows are domed shaped just like the earth. The real truth about what happened that day will be revealed after the movie ends. If you're not ready for it now. Or you think you know the truth about what happened that day, wait until thus shitshow movie ends. I've released a ton of intel drops here. If you're just joining me. Goto my profile page here, and in the search bar, type in any subject. For example, type in 911. Planes, etc. There, you will find just about all of my detailed 💊 and intel drops. Key points to remember about 911. - No planes hit the buildings - The planes where cgi on television and holograms[project blue beam tech] in person. In some locations where you couldn't see the holograms. It just exploded - Thus the hijackers were all faked. No Muslims hijacked no planes. This planed by the zionist cabal - No gold was taken in the middle of the night. Those black trucks rescued 95-99% of the victims. Victims are safe in Antarctica. Another reason why they don't want you there[watch the waters] - War on terror is fake. It was created to chip away your rights from the constitution. Which ultimately did away with it. Now you just have an illusion of one If no one is exposing these aforementioned truths. Then they're either controlled by the Octogon Group. Or fell for it all. I'm quite sure they ran across something that countered the official narrative. And simply ignored it. Which means they are controlled. My mission here is to expose the cabal and their plans. Join me. 💯

WayneTech SPFX®️

602,844 просмотров • 3 лет назад

Doing another DMSO review because I posted about it back in May and got roughly 900 comments in response. Wanted to summarize what people actually reported. Screenshot ready. These are the top 10 uses ranked by how frequently they were mentioned in the comments: Pain and joint relief. By far the most mentioned. DMSO is anti-inflammatory and blocks pain signals. I've shared the cayenne salve recipe a few times and DMSO significantly enhances it. Try it once and you might be shocked at how effective it is. Eye issues. DMSO is effective at delivering therapeutic compounds. Multiple people reported using it for various eye issues. I haven't used it this way, so share in the comments if you have. Everyday use. Both topical and oral. Water and fat soluble properties make it uniquely versatile. Many reported it as part of their daily routine. Skin issues. DMSO carries whatever's on your skin rapidly across into the bloodstream. If you have a topical you want to work better, DMSO amplifies it. Stroke recovery. Dental and gum health. Urology. Veterinary use. General wellness tonic. Nerve and spinal support. A few things worth knowing: Apply DMSO only to clean, dry skin. It carries whatever is on the surface (lotion, sunscreen, residue) into your bloodstream. Wash and dry the area first. You may notice a garlic-like taste in your mouth after using DMSO. That's normal. It comes from the sulfur compound metabolizing in your system. Store in a sealed glass container in a cool, dark spot. Long shelf life when stored properly. Buy pharmaceutical grade DMSO from reputable sources.

Doing another DMSO review because I posted about it back in May and got roughly 900 comments in response. Wanted to summarize what people actually reported. Screenshot ready. These are the top 10 uses ranked by how frequently they were mentioned in the comments: Pain and joint relief. By far the most mentioned. DMSO is anti-inflammatory and blocks pain signals. I've shared the cayenne salve recipe a few times and DMSO significantly enhances it. Try it once and you might be shocked at how effective it is. Eye issues. DMSO is effective at delivering therapeutic compounds. Multiple people reported using it for various eye issues. I haven't used it this way, so share in the comments if you have. Everyday use. Both topical and oral. Water and fat soluble properties make it uniquely versatile. Many reported it as part of their daily routine. Skin issues. DMSO carries whatever's on your skin rapidly across into the bloodstream. If you have a topical you want to work better, DMSO amplifies it. Stroke recovery. Dental and gum health. Urology. Veterinary use. General wellness tonic. Nerve and spinal support. A few things worth knowing: Apply DMSO only to clean, dry skin. It carries whatever is on the surface (lotion, sunscreen, residue) into your bloodstream. Wash and dry the area first. You may notice a garlic-like taste in your mouth after using DMSO. That's normal. It comes from the sulfur compound metabolizing in your system. Store in a sealed glass container in a cool, dark spot. Long shelf life when stored properly. Buy pharmaceutical grade DMSO from reputable sources.

Rob Benson

82,625 просмотров • 16 дней назад

A friend of mine works in market research Her job involves calling different brands, asking questions, and collecting insights that later turn into reports. She needs to capture everything accurately while the conversation is still happening Earlier, she used to note everything down manually during the call. It was stressful and she often had to spend extra time later cleaning up messy notes. Now she does something much simpler. She keeps the call on speaker on her laptop, turns on the Typeless mic, and it converts the conversation into clean notes in real time. After the call, she just asks it to turn those notes into a proper summary or email for her team. No scrambling through rough notes. No spending another 20 minutes rewriting everything. She even does the same thing on mobile when she needs to send a quick update after a call. What makes it powerful is how it handles real speech. We don’t talk in perfect sentences. We pause, repeat ourselves, change direction mid-sentence, and add filler words. Typeless understands that. It removes filler words, fixes repetitions, catches mid-sentence changes, and turns messy speech into clean, ready to send text, whether that’s an email, a bullet list, notes, or a message. One feature that really stands out is voice based editing. You can refine sentences, change the tone, or tweak wording just by speaking again. No typing needed. It works anywhere you write WhatsApp, Slack, email, Notes, even ChatGPT and supports 100+ languages with a personal dictionary. Privacy is also a big focus. The platform is HIPAA compliant and GDPR compliant, with zero cloud data retention. Your data is never used to train AI models, and your history stays stored locally on your device. For professionals working with sensitive information, that matters a lot. They’re also working toward SOC 2 Type II and ISO 27001 compliance. Speaking your thoughts and getting polished writing instantly is honestly much faster than typing everything. That’s basically what Typeless does. Try it here: Android: (check the video 👇) Trust center:

A friend of mine works in market research Her job involves calling different brands, asking questions, and collecting insights that later turn into reports. She needs to capture everything accurately while the conversation is still happening Earlier, she used to note everything down manually during the call. It was stressful and she often had to spend extra time later cleaning up messy notes. Now she does something much simpler. She keeps the call on speaker on her laptop, turns on the Typeless mic, and it converts the conversation into clean notes in real time. After the call, she just asks it to turn those notes into a proper summary or email for her team. No scrambling through rough notes. No spending another 20 minutes rewriting everything. She even does the same thing on mobile when she needs to send a quick update after a call. What makes it powerful is how it handles real speech. We don’t talk in perfect sentences. We pause, repeat ourselves, change direction mid-sentence, and add filler words. Typeless understands that. It removes filler words, fixes repetitions, catches mid-sentence changes, and turns messy speech into clean, ready to send text, whether that’s an email, a bullet list, notes, or a message. One feature that really stands out is voice based editing. You can refine sentences, change the tone, or tweak wording just by speaking again. No typing needed. It works anywhere you write WhatsApp, Slack, email, Notes, even ChatGPT and supports 100+ languages with a personal dictionary. Privacy is also a big focus. The platform is HIPAA compliant and GDPR compliant, with zero cloud data retention. Your data is never used to train AI models, and your history stays stored locally on your device. For professionals working with sensitive information, that matters a lot. They’re also working toward SOC 2 Type II and ISO 27001 compliance. Speaking your thoughts and getting polished writing instantly is honestly much faster than typing everything. That’s basically what Typeless does. Try it here: Android: (check the video 👇) Trust center:

aditii

59,829 просмотров • 6 месяцев назад

if you are using someone else's garbage saas ai design review product, stop... all of if it is waste of time and i'll give you something better for free that runs with whatever analysis tools you want. altium-cruncher 2026.6.7 public release kicad-cruncher 2026.6.7 public release altium-cruncher is cli tool built on the altium-monkey library. (kicad-cruncher is built on kicad-monkey) here we show the "design_review" (dr) command dr converts an altium project into a simple graph-spatial-topological format specifical design for llm's - json netlist with links with detail indexes into the schematic SVG elements/groups - enriched pcb SVG heavily decorated with links back to the the json graph - all schematic notes in json form - SVG schematic/pcb elements are heirachically grouped w/ tags for the llm to build its own internal map of subcircuits with linkage to copper artwork subsections. - full part parameter linkage - an .md with a guide for codex/claude on how to step through the eviscerated project. - bonus: schdoc and pcbdoc in a raw json serialized format (what altium uses internally but doesnt give you from teh gui). this format is the literal json form of the altium format essentially all of the resolved schematic graph, physical topology, layer stack information and metadata to do make best use of current frontier and local llm techbology it actually was an accidental discovery. better than a netlist. no vendor lock-in. gives you some help without pretending its 100% . no need to waste tokens on full image processing,etc. this dataset works better, is simpler, and compatible with local llms. can help save some time and give you another set of eyes before a release. works on win/mac/linux gratis!

if you are using someone else's garbage saas ai design review product, stop... all of if it is waste of time and i'll give you something better for free that runs with whatever analysis tools you want. altium-cruncher 2026.6.7 public release kicad-cruncher 2026.6.7 public release altium-cruncher is cli tool built on the altium-monkey library. (kicad-cruncher is built on kicad-monkey) here we show the "design_review" (dr) command dr converts an altium project into a simple graph-spatial-topological format specifical design for llm's - json netlist with links with detail indexes into the schematic SVG elements/groups - enriched pcb SVG heavily decorated with links back to the the json graph - all schematic notes in json form - SVG schematic/pcb elements are heirachically grouped w/ tags for the llm to build its own internal map of subcircuits with linkage to copper artwork subsections. - full part parameter linkage - an .md with a guide for codex/claude on how to step through the eviscerated project. - bonus: schdoc and pcbdoc in a raw json serialized format (what altium uses internally but doesnt give you from teh gui). this format is the literal json form of the altium format essentially all of the resolved schematic graph, physical topology, layer stack information and metadata to do make best use of current frontier and local llm techbology it actually was an accidental discovery. better than a netlist. no vendor lock-in. gives you some help without pretending its 100% . no need to waste tokens on full image processing,etc. this dataset works better, is simpler, and compatible with local llms. can help save some time and give you another set of eyes before a release. works on win/mac/linux gratis!

Eli Hughes

16,263 просмотров • 3 месяцев назад

GeoLibre v2.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings a legend that writes itself from your symbology, a new GeoLens catalog browser, and 200+ GeoLibre Rust geoprocessing tools running entirely in the browser. What's new in v2.3.0 - Automatic on-map Legend: the legend builds itself from your visible layers, with class rows for graduated, categorized, rule-based, and expression styling, gradient bars for heatmaps and raster colormaps, and land-cover labels from a Raster Attribute Table. Rename, hide, reorder, or add your own entries, and it saves with the project. - Symbology swatches in the Layers panel: every row shows a dot, line, square, or image glyph in the layer's own color, so a tall layer stack reads at a glance. - GeoLens catalog browser: connect to a self-hosted GeoLens server, search its catalog, and add datasets as vector tiles, GeoJSON, or rendered raster tiles. - Emerging Hot Spot Analysis: build a space-time cube from timestamped points and classify every cell as a new, intensifying, persistent, diminishing, sporadic, oscillating, or historical hot or cold spot, all client side. - Mosaic time series: the Time Slider now steps through MosaicJSON and STAC collections of many COGs per date, on either a GPU or a WASM rendering engine. - Copy and paste layer styles: give a whole set of layers one consistent look without restyling each in turn. - Shareable tool links: deep-link any Whitebox tool with a ?tool= URL that opens the dialog preselected and pre-fills the form, with a Copy link button to build it for you. - Smarter data loading: pick which layers to load from a multi-layer GeoPackage, import CSVs whose coordinates are in any projected CRS, and read a raster's real CRS, pixel size, and extent from the metadata dialog. - Multiple AI profiles: define several provider, model, and credential setups, pick a default, and switch between them from the assistant panel. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #MapLibre #GeoLibre

GeoLibre v2.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release brings a legend that writes itself from your symbology, a new GeoLens catalog browser, and 200+ GeoLibre Rust geoprocessing tools running entirely in the browser. What's new in v2.3.0 - Automatic on-map Legend: the legend builds itself from your visible layers, with class rows for graduated, categorized, rule-based, and expression styling, gradient bars for heatmaps and raster colormaps, and land-cover labels from a Raster Attribute Table. Rename, hide, reorder, or add your own entries, and it saves with the project. - Symbology swatches in the Layers panel: every row shows a dot, line, square, or image glyph in the layer's own color, so a tall layer stack reads at a glance. - GeoLens catalog browser: connect to a self-hosted GeoLens server, search its catalog, and add datasets as vector tiles, GeoJSON, or rendered raster tiles. - Emerging Hot Spot Analysis: build a space-time cube from timestamped points and classify every cell as a new, intensifying, persistent, diminishing, sporadic, oscillating, or historical hot or cold spot, all client side. - Mosaic time series: the Time Slider now steps through MosaicJSON and STAC collections of many COGs per date, on either a GPU or a WASM rendering engine. - Copy and paste layer styles: give a whole set of layers one consistent look without restyling each in turn. - Shareable tool links: deep-link any Whitebox tool with a ?tool= URL that opens the dialog preselected and pre-fills the form, with a Copy link button to build it for you. - Smarter data loading: pick which layers to load from a multi-layer GeoPackage, import CSVs whose coordinates are in any projected CRS, and read a raster's real CRS, pixel size, and extent from the metadata dialog. - Multiple AI profiles: define several provider, model, and credential setups, pick a default, and switch between them from the assistant panel. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #RemoteSensing #MapLibre #GeoLibre

Qiusheng Wu

294,059 просмотров • 1 месяц назад

Alright, this one’s worth your attention if you’re building or deploying agents. Future AGI just open-sourced their entire platform and i don’t mean a trimmed-down version. this is the full stack: UI, backend, simulation engine, evals, optimization loop, observability, guardrails, gateway, docs. all in one repo. Apache 2.0. I’ve been putting it through its paces on production agents, and what stands out isn’t just the breadth it’s the architecture. Most of the current “agent reliability” stack is fragmented. tracing lives in one tool, evals in another, guardrails somewhere else. you end up manually connecting dots, and the agent itself doesn’t really improve you just keep patching prompts and hoping for the best. This flips that model. It’s built as a closed feedback loop: simulate failures → evaluate in real time → detect production issues → learn from them → generate fixes → validate against real traffic → check regressions → redeploy → monitor again And when something new breaks, the loop just runs again. no manual glue. The simulation piece is especially strong. instead of static test cases, it generates adversarial, multi-turn conversations based on how your agent actually behaves basically hunting for the exact scenarios where your system fails confidently. ran a few thousand simulations on our side… caught things we definitely would’ve missed. Evals run fast (sub-50ms) across modalities. not LLM-as-judge trained classifiers. guardrails are built-in, not layered on top. observability gives you step-level visibility into reasoning, cost, latency, quality. But the real shift is the optimization loop. Most tools tell you *what* broke. this system actually fixes it, validates the fix, and ensures nothing else regresses. That’s the missing layer. It’s clearly built with production in mind not a research demo. and the fact that it’s self-hostable makes it even more relevant if you’re running serious workloads. If you’ve been duct-taping together infra around your agents, this is probably the closest thing to a unified system i’ve seen so far. Worth checking out. If you're serious about deploying reliable AI agents, this is worth a look: 👉 You can also try it instantly (no setup) via their cloud version:

Alright, this one’s worth your attention if you’re building or deploying agents. Future AGI just open-sourced their entire platform and i don’t mean a trimmed-down version. this is the full stack: UI, backend, simulation engine, evals, optimization loop, observability, guardrails, gateway, docs. all in one repo. Apache 2.0. I’ve been putting it through its paces on production agents, and what stands out isn’t just the breadth it’s the architecture. Most of the current “agent reliability” stack is fragmented. tracing lives in one tool, evals in another, guardrails somewhere else. you end up manually connecting dots, and the agent itself doesn’t really improve you just keep patching prompts and hoping for the best. This flips that model. It’s built as a closed feedback loop: simulate failures → evaluate in real time → detect production issues → learn from them → generate fixes → validate against real traffic → check regressions → redeploy → monitor again And when something new breaks, the loop just runs again. no manual glue. The simulation piece is especially strong. instead of static test cases, it generates adversarial, multi-turn conversations based on how your agent actually behaves basically hunting for the exact scenarios where your system fails confidently. ran a few thousand simulations on our side… caught things we definitely would’ve missed. Evals run fast (sub-50ms) across modalities. not LLM-as-judge trained classifiers. guardrails are built-in, not layered on top. observability gives you step-level visibility into reasoning, cost, latency, quality. But the real shift is the optimization loop. Most tools tell you what broke. this system actually fixes it, validates the fix, and ensures nothing else regresses. That’s the missing layer. It’s clearly built with production in mind not a research demo. and the fact that it’s self-hostable makes it even more relevant if you’re running serious workloads. If you’ve been duct-taping together infra around your agents, this is probably the closest thing to a unified system i’ve seen so far. Worth checking out. If you're serious about deploying reliable AI agents, this is worth a look: 👉 You can also try it instantly (no setup) via their cloud version:

Aakash Verma

22,915 просмотров • 4 месяцев назад