project rollup
Default
16729
mentions
across 19 keywords
across 19 keywords
net sentiment
+24%
5266 positive
10235 neutral
1228 negative
volume & sentiment over time
May 13 – Oct 16, 2026 · 4-day buckets
1–8 of 8 keywords
| keyword | mentions | net | trend | top theme | srcs | last seen | |
|---|---|---|---|---|---|---|---|
| timeline | 16428 | +62% | — | 1 | 2h | ||
| Opus 5.5 model documentation | 51 | +61% | claude / pricing | 2 | 2w | ||
| Opus 5.5 official announcement | 50 | +68% | pricing / model | 1 | 2w | ||
| SynthID | 16 | +100% | gemini / google | 1 | 2w | ||
| Cline | 149 | +72% | code | 3 | 2w | ||
| Quill | 32 | -10% | performance | 4 | 2w | ||
| Google SynthID AI image watermark discussion | 1 | +100% | — | 1 | 2mo | ||
| Opus 5.5 versus Claude Opus models | 2 | +100% | — | 1 | 3mo |
mentions
Showing 12851–12875 of 16729 mentions
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I still get a weird amount of satisfaction from seeing something I built being used by a stranger. I still get a weird amount of satisfaction from seeing something I built being used by a stranger.view source ↗
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This was even worse than Sambhav Jain's tragedy 🥲 > Rahul Balyan, a Noida gym trainer who had two gyms > A newly married doctor joined his gym > She was happily married and her husband was also earning well > After she started coming to the gym, things apparently changed. The trainer started giving almost all his time… This was even worse than Sambhav Jain's tragedy 🥲 > Rahul Balyan, a Noida gym trainer who had two gyms > A newly married doctor joined his gym > She was happily married and her husband was also earning well > After she started coming to the gym, things apparently changed. The trainer started giving almost all his time to her > They would spend hours together, giggling and hanging around in the gym. This continued for months > Then she suddenly stopped coming for a few weeks. Later, people came to know that her husband had allegedly found the trainer and his wife together in an intimate situation in his own room > He went to court and filed for divorce > This woman had no regret and as she was unhappy with her sex life with her husband. She continued spending time with the trainer > Even after the husband presented evidence of adultery, he still had to pay alimony > The woman was a well earning doctor herself, yet the husband had to pay her maintenance just because he was earning more > He tried to start a new life, but that bully trainer allegedly kept sending pictures of him enjoying life with the doctor through different numbers, just to hurt him > Alimony and everything that followed apparently broke him down. Eventually, he ended his life > The trainer and the doctor moved on with their lives > His parents have filed a case in court which is still pending This is not about men vs women. Men cheat too. But even when a wife cheats, it's the husband who has to pay for her sins because adultery isn't a crime now 🤡🙏view source ↗
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Use App Store Connect CLI claude/codex can handle: > subscriptions > one-time purchases > fill in all necessary parts > prepare and upload screenshots > send to TestFlight > send for review You don't need to open the ASC page in your browser https://t.co/I2K6o54P8l Use App Store Connect CLI claude/codex can handle: > subscriptions > one-time purchases > fill in all necessary parts > prepare and upload screenshots > send to TestFlight > send for review You don't need to open the ASC page in your browser https://t.co/I2K6o54P8lview source ↗
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RT @HermesWatcher: Hermes has an entire library of built-in automations sitting behind one command: /blueprint There are 16 Blueprints cov… RT @HermesWatcher: Hermes has an entire library of built-in automations sitting behind one command: /blueprint There are 16 Blueprints cov…view source ↗
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🚨 Mukesh Kumar demands the ball directly from Sarfaraz Khan and takes a huge wicket right after! 🗣 “Just give me One more over and I’ll give you wickets!” Calls his own shot, gets the trust, delivers the big breakthrough. https://t.co/lGqR6G5Dq5 🚨 Mukesh Kumar demands the ball directly from Sarfaraz Khan and takes a huge wicket right after! 🗣 “Just give me One more over and I’ll give you wickets!” Calls his own shot, gets the trust, delivers the big breakthrough. https://t.co/lGqR6G5Dq5view source ↗
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day 2 → someone offers $5k for my app day 4 → a yc backed founder reached out about https://t.co/w4yqPKlw9D 👀 what is HAPPENING 🤯 am i speedrunning this or what? https://t.co/YcnITJeyoE day 2 → someone offers $5k for my app day 4 → a yc backed founder reached out about https://t.co/w4yqPKlw9D 👀 what is HAPPENING 🤯 am i speedrunning this or what? https://t.co/YcnITJeyoEview source ↗
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Meet Sai Bharath, a https://t.co/YSvOF5fxhn 2023 Computer Science graduate from NIT Warangal. Despite being born without his left hand, he has pursued his dreams and is now working as an SDE-II at Amazon, earning 50+ LPA. He is currently working on the Amazon Pay app as a standalone application rather than as part of… Meet Sai Bharath, a https://t.co/YSvOF5fxhn 2023 Computer Science graduate from NIT Warangal. Despite being born without his left hand, he has pursued his dreams and is now working as an SDE-II at Amazon, earning 50+ LPA. He is currently working on the Amazon Pay app as a standalone application rather than as part of the Amazon Shopping app. His journey is a reminder that physical challenges don't define a person's abilities or limit their potential. FYI check :- https://t.co/P4H36WHUY5view source ↗
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Bro Built Facebook, Instagram, WhatsApp and Meta… still working like an intern https://t.co/WiErj80HdL Bro Built Facebook, Instagram, WhatsApp and Meta… still working like an intern https://t.co/WiErj80HdLview source ↗
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You are starting a company today and expect huge traffic within 3 years. What are you choosing? A. Next.js + TypeScript + Node.js + PostgreSQL + AWS B. React + Go + PostgreSQL + Redis + Kubernetes C. Next.js + Python + FastAPI + PostgreSQL + AWS D. SvelteKit + Rust + PostgreSQL + Cloudflare You are starting a company today and expect huge traffic within 3 years. What are you choosing? A. Next.js + TypeScript + Node.js + PostgreSQL + AWS B. React + Go + PostgreSQL + Redis + Kubernetes C. Next.js + Python + FastAPI + PostgreSQL + AWS D. SvelteKit + Rust + PostgreSQL + Cloudflareview source ↗
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RT @elsleightholm: No, AI didn’t just solve the Navier-Stokes equations. So what did it do? https://t.co/BJZqofVqlQ RT @elsleightholm: No, AI didn’t just solve the Navier-Stokes equations. So what did it do? https://t.co/BJZqofVqlQview source ↗
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Anthropic valuation - $2 trillion Samsung valuation - $1.35 trillion Anthropic 2025 revenue - $4.6 billion Samsung 2025 revenue - $236 billion https://t.co/xCGIpAoYzQ Anthropic valuation - $2 trillion Samsung valuation - $1.35 trillion Anthropic 2025 revenue - $4.6 billion Samsung 2025 revenue - $236 billion https://t.co/xCGIpAoYzQview source ↗
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https://t.co/AzBuIW8eDq the kind of portfolio I’d love to have someday. absolute legend behind so much of what makes @zerodha what it is. https://t.co/AzBuIW8eDq the kind of portfolio I’d love to have someday. absolute legend behind so much of what makes @zerodha what it is.view source ↗
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I just want my manager not to see this. (He will😭) I just want my manager not to see this. (He will😭)view source ↗
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A Russian drone slammed into a bridge in the Ukrainian capital of Kyiv on Sunday as German Chancellor Friedrich Merz visited the city in a display of support for Ukrainian President Volodymyr Zelensky. https://t.co/dgk8gb2Waq https://t.co/vFvbGg4FY6 A Russian drone slammed into a bridge in the Ukrainian capital of Kyiv on Sunday as German Chancellor Friedrich Merz visited the city in a display of support for Ukrainian President Volodymyr Zelensky. https://t.co/dgk8gb2Waq https://t.co/vFvbGg4FY6view source ↗
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The ultimate goal isn't to look successful. It's to feel fulfilled. The ultimate goal isn't to look successful. It's to feel fulfilled.view source ↗
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A worthy burden can organize a life that pleasure has scattered. A worthy burden can organize a life that pleasure has scattered.view source ↗
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Find new customers and write the emails to reach them. My Hermes Agent OS does the whole flow: → Tell it who you want to reach → Hermes finds matching leads → https://t.co/joCmLmxHOd finds their emails → AI writes each email → You check and approve No email goes out until you say so. Want the SOP? DM me. Find new customers and write the emails to reach them. My Hermes Agent OS does the whole flow: → Tell it who you want to reach → Hermes finds matching leads → https://t.co/joCmLmxHOd finds their emails → AI writes each email → You check and approve No email goes out until you say so. Want the SOP? DM me.view source ↗
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A truck is running 47 minutes late. The AI agent already found a faster route. One click approves it, the ETA rolls back inside the window, and the customer gets live tracking. Would you let AI reroute your trucks automatically, or approve every change yourself? A truck is running 47 minutes late. The AI agent already found a faster route. One click approves it, the ETA rolls back inside the window, and the customer gets live tracking. Would you let AI reroute your trucks automatically, or approve every change yourself?view source ↗
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It looks unbelievably simple. It was unbelievably complicated. A while back, Jon asked, "Why couldn't we just build a tool that people can use to refer people?" So I built one. You put your name in. You put your friend's name in. It finds their photo and drops you both into a retro video-game invitation. Player … It looks unbelievably simple. It was unbelievably complicated. A while back, Jon asked, "Why couldn't we just build a tool that people can use to refer people?" So I built one. You put your name in. You put your friend's name in. It finds their photo and drops you both into a retro video-game invitation. Player 1. Player 2. Every click plays a little 8-bit sound. When I showed my coach Angus, he said those sounds are "kind of like dopamine hits." Then Sandra Newson left a comment: let every attendee add one personal sentence to their invitation. So now you can add a personal note. Thank you, Sandra. I made the art and the little avatars in ChatGPT. I knocked out the photo holes in Photoshop. I started coding in Codex and moved over to Claude Code. About five hours. But the line that stuck with me was Angus's: "It becomes a contagion. If somebody gets this, they want to be a part of it." That's the whole idea behind getting to 3,000. The people already coming invite the people they'd want next to them. Want to try it live? Tell me in the comments and I'll send you the link. 👇 Press Start: The Road to 3,000. #SocialMediaExaminer #SMMW27 #RoadTo3000 #AIinMarketing #EventMarketingview source ↗
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Tried adding WebGL shaders as an option inside the skill and it turned out pretty sick. (one-shotted using the skill) Asked it to create a Raptor engine from @SpaceX that comes apart on hover, then ignites using WebGL. Still a prototype, but the result is pretty wild. https://t.co/7J27o7X5DW Tried adding WebGL shaders as an option inside the skill and it turned out pretty sick. (one-shotted using the skill) Asked it to create a Raptor engine from @SpaceX that comes apart on hover, then ignites using WebGL. Still a prototype, but the result is pretty wild. https://t.co/7J27o7X5DWview source ↗
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Meta. Google. Microsoft. These companies have spent years engineering faster vector-search systems. A basic nearest-neighbor query computes the distance between the query and every stored vector. Its cost grows linearly with the size of the database and becomes impractical across millions of candidates. Every vecto… Meta. Google. Microsoft. These companies have spent years engineering faster vector-search systems. A basic nearest-neighbor query computes the distance between the query and every stored vector. Its cost grows linearly with the size of the database and becomes impractical across millions of candidates. Every vector index reduces this computation differently, using clustering, graph traversal, compression, or a combination of these methods. Here are five common approaches: 1. Flat index A Flat index compares the query with every stored vector, selects the K smallest distances, and returns them. It performs an exact search, so it is useful when accuracy matters and the dataset is small enough to scan. The query cost grows linearly with the number of stored vectors. 2. IVF IVF divides the vector space into clusters. Each stored vector is assigned to its nearest cluster centroid. At query time, the database finds the closest centroids and searches only their clusters. A probe parameter controls how many clusters it searches, and increasing it usually improves recall but also increases latency. 3. HNSW HNSW stores vectors in a multilayer graph. The upper layers contain fewer nodes and allow large jumps through the graph. Lower layers contain more nodes and support a finer search. A query starts at the top, moves toward closer nodes, and descends through the layers. HNSW often provides low latency and high recall, but the graph requires extra memory. 4. IVF-PQ IVF-PQ combines clustering with product quantization. IVF limits the search to a few relevant clusters. Product quantization splits each vector into smaller parts and stores compact codes for them. Comparing these codes is cheaper than comparing full-precision vectors. This reduces memory use and query cost, with some loss in recall. 5. ScaNN ScaNN partitions the dataset and stores quantized vector representations. During search, it selects relevant partitions, scores compressed candidates,view source ↗
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I love SI slopcore I love SI slopcoreview source ↗
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RT @ghita__ha: when the thing you built 2 years ago suddenly becomes cool again 😂 RT @ghita__ha: when the thing you built 2 years ago suddenly becomes cool again 😂view source ↗
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Today x gives me ballons.. (Actually it didn't, hahah,wtf x?😝) Thank God for every day!! 40 here I come.. https://t.co/xLj42V2EZB Today x gives me ballons.. (Actually it didn't, hahah,wtf x?😝) Thank God for every day!! 40 here I come.. https://t.co/xLj42V2EZBview source ↗
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The reason designers can't build sites like this isn't talent. It's that nobody tells them step 1. Save this. Repost it so another designer finds it too. Here's how it actually works 👇 1. It's not a video. It's hundreds of still images played in order - like a flipbook. Scrolling replaces play/pause. 2. One image s… The reason designers can't build sites like this isn't talent. It's that nobody tells them step 1. Save this. Repost it so another designer finds it too. Here's how it actually works 👇 1. It's not a video. It's hundreds of still images played in order - like a flipbook. Scrolling replaces play/pause. 2. One image stays fixed on screen the whole time. Everything else just decides which frame of the flipbook you're looking at. 3. Your scroll position = one number. That single number controls everything - which image shows, how zoomed in it is, when the text changes. 4. Break the site into "scenes," like chapters. Scene 1 plays for the first bit of scrolling, then scene 2 takes over, and so on. 5. Between scenes, don't just cut - blend. A slow dissolve or wipe between images is what makes it feel expensive instead of choppy. 6. Load images smart: rough version first, sharp version once the reader scrolls close. That's why it never lags or freezes. 7. Text appears and disappears on its own scroll range too - tied to that same number, so it's never out of sync with the images. That's the whole trick. No video editor, no complicated code - just images, one number, and good timing.view source ↗