project rollup
Default
16178
mentions
across 19 keywords
across 19 keywords
net sentiment
+24%
5105 positive
9895 neutral
1178 negative
volume & sentiment over time
May 13 – Oct 14, 2026 · 4-day buckets
1–8 of 8 keywords
| keyword | mentions | net | trend | top theme | srcs | last seen | |
|---|---|---|---|---|---|---|---|
| timeline | 15877 | +63% | — | 1 | 2d | ||
| 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 13626–13650 of 16178 mentions
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government of India officially removes bitchat from the App Store https://t.co/9oHHiwquec government of India officially removes bitchat from the App Store https://t.co/9oHHiwquecview source ↗
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guess the linux distro https://t.co/rtJSAph2Th guess the linux distro https://t.co/rtJSAph2Thview source ↗
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Lauren Tan is shipping 1000 of PRs a month with AI agents Matt Pocock just sat down with her to unpack how the system actually works: · 04:35 - how Lauren got to thousands of PRs without drowning in AI slop · 13:20 - why pstack starts with skills, not bigger prompts · 24:10 - verification: make the agent prove its wo… Lauren Tan is shipping 1000 of PRs a month with AI agents Matt Pocock just sat down with her to unpack how the system actually works: · 04:35 - how Lauren got to thousands of PRs without drowning in AI slop · 13:20 - why pstack starts with skills, not bigger prompts · 24:10 - verification: make the agent prove its work before you trust it · 36:45 - redesign the codebase so agents naturally take the right path · 49:30 - how to turn one coding agent into a high-velocity software factory the biggest lesson isn’t “run more agents.” it’s making one agent trustworthy enough that you can safely run 10, 20, then hundreds of tasks in parallel. skills → constraints → verification → cloud agents → parallel execution Lauren basically treats the codebase as infrastructure for the agents themselves. Humans decide what matters. Agents do the work, prove it works, and keep the loop moving. Probably one of the best conversations on agentic engineering right now. watch it before you add another 20-page prompt to your coding agent.view source ↗
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If you need 35k verified follower Just Say - Yes 👋 We will boost your page. https://t.co/c8v88RPXHW If you need 35k verified follower Just Say - Yes 👋 We will boost your page. https://t.co/c8v88RPXHWview source ↗
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Day 4 of growing https://t.co/qfvrRN31eg Goal 5,000 daily users. We crossed 2k users in a day & are now over 1k daily users 1/5 of the way there! https://t.co/3f1xZEQqOb Day 4 of growing https://t.co/qfvrRN31eg Goal 5,000 daily users. We crossed 2k users in a day & are now over 1k daily users 1/5 of the way there! https://t.co/3f1xZEQqObview source ↗
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Ocra is a typo 😆 Orca? It's an ADE: Agentic Development Environment, which is an IDE built specifically for working with A LOT of agents. Designed for builders, developers and teams, looking to 100x their workflow with AI. Give it a spin?? 🫶 https://t.co/bcdLbl5WME Ocra is a typo 😆 Orca? It's an ADE: Agentic Development Environment, which is an IDE built specifically for working with A LOT of agents. Designed for builders, developers and teams, looking to 100x their workflow with AI. Give it a spin?? 🫶 https://t.co/bcdLbl5WMEview source ↗
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ولي عهد دبي يزور الطيار الهندي المصاب في حادثة "فلاي دبي" https://t.co/vpdO5V16C9 ولي عهد دبي يزور الطيار الهندي المصاب في حادثة "فلاي دبي" https://t.co/vpdO5V16C9view source ↗
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Trending repository of the day 📈 Agent-Reach Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. Last 24h: 1,683 ⭐ Total: 89,354 ⭐️ https://t.co/BUp2aLeFkM Trending repository of the day 📈 Agent-Reach Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. Last 24h: 1,683 ⭐ Total: 89,354 ⭐️ https://t.co/BUp2aLeFkMview source ↗
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Update for 2026: Just learn to sell. AI will do the rest. Update for 2026: Just learn to sell. AI will do the rest.view source ↗
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i got to $120k revenue without doing half of this. no product hunt. no 50 chatgpt blog posts. no facebook ads. what actually got me my first 100 users (no bs): 1) dm'ing people who had already complained about the exact problem i was trying to solve on LinkedIn and X. keep it short and simple. 2) replying under eve… i got to $120k revenue without doing half of this. no product hunt. no 50 chatgpt blog posts. no facebook ads. what actually got me my first 100 users (no bs): 1) dm'ing people who had already complained about the exact problem i was trying to solve on LinkedIn and X. keep it short and simple. 2) replying under every post in the niche for 30 days. not “check out my tool.” actual answers with insights. 3) obsessing over the first 20 users. be it calls, loom videos, or fixing their setup myself. 4) posting the $$ numbers. $0. then $10k. people trust authenticity. ads and blogs only start working after some users are already paying you. before that 90% of the job is just talking to people who already have the problem. are you stuck at getting users for your product? comment the product link and i’ll tell you how i'll go about acquiring your first 100 users. first 10 comments get a loom video. Disclaimer - I lead growth at a YC startupview source ↗
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Hopefully it is going to be the last meeting with my lawyer on this stupid case. Looking back, that 1 lakh of security deposit (my share on the amount) was absolutely negligible compared to the amount of trauma that came from trying to retrieve it. It costed me more money and also so much time. Also had to deal with t… Hopefully it is going to be the last meeting with my lawyer on this stupid case. Looking back, that 1 lakh of security deposit (my share on the amount) was absolutely negligible compared to the amount of trauma that came from trying to retrieve it. It costed me more money and also so much time. Also had to deal with the worst kinds of humans in this process. One of the flatmates decided to give up their share of the amount and chose peace over all this fighting and I think they made the right decision. It is important to analyse the second order effects of decisions. 1 lakh in hindsight was nothing and I should have just left it.view source ↗
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I'm not sure I ever jumped on a purchase button as quick as I did on @uiarcdev. It's absolutely stunning. I'm not sure I ever jumped on a purchase button as quick as I did on @uiarcdev. It's absolutely stunning.view source ↗
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BREAKING: The U.S. Coast Guard has launched a search for a missing Boston-bound plane off the coast of Nantucket after the FAA lost contact with the aircraft carrying six people. Get the latest updates with the Fox News app. Download here: https://t.co/Q8qTIP81ow https://t.co/OXds6NhCDP BREAKING: The U.S. Coast Guard has launched a search for a missing Boston-bound plane off the coast of Nantucket after the FAA lost contact with the aircraft carrying six people. Get the latest updates with the Fox News app. Download here: https://t.co/Q8qTIP81ow https://t.co/OXds6NhCDPview source ↗
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this app makes $800,000/mo that's literally $9.6M/year they are running organic AI UGC & clipping campaigns each of these videos are generating 1M-3M views: https://t.co/6jirsGelbE this app makes $800,000/mo that's literally $9.6M/year they are running organic AI UGC & clipping campaigns each of these videos are generating 1M-3M views: https://t.co/6jirsGelbEview source ↗
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to all you doubters, Gemini 4 Argon now beats Opus 4.8 on Agent Arena! https://t.co/9JlvJAmsFQ to all you doubters, Gemini 4 Argon now beats Opus 4.8 on Agent Arena! https://t.co/9JlvJAmsFQview source ↗
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Anthropic just revealed a trick to improve how Claude Opus 5.5 works. The idea is that Opus does the work while Fable 5.1 acts as a second opinion. All you have to do is open Claude Code and run: /advisor fable When Opus consults Fable, it gets the full context of the session, including the tools used and their res… Anthropic just revealed a trick to improve how Claude Opus 5.5 works. The idea is that Opus does the work while Fable 5.1 acts as a second opinion. All you have to do is open Claude Code and run: /advisor fable When Opus consults Fable, it gets the full context of the session, including the tools used and their results. It can be especially useful: → Before creating a large plan → When the same error keeps happening → Before marking a task as finished This gives you a second review without having to copy context between conversations. The feature is still experimental, requires access to Fable, and uses additional tokens. 🔖 Save this for your next session.view source ↗
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India's target was 4 runs KL Rahul 's target was 8 runs Bro did the math and completed both assignments in two balls 😭🔥 https://t.co/Na5cfPQE9V India's target was 4 runs KL Rahul 's target was 8 runs Bro did the math and completed both assignments in two balls 😭🔥 https://t.co/Na5cfPQE9Vview source ↗
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My personal brand crossed 2.8 million people. 290k on YT. 270k on email. 960K IG. 950K LI. Threads 258K. TikTok 147K. 4 things that did the compounding: 1. One platform at a time, go deep 2. Voice memos over writer's block 3. Waterfalling winners every 90 days 4. A newsletter that owns the relationship Slow is sm… My personal brand crossed 2.8 million people. 290k on YT. 270k on email. 960K IG. 950K LI. Threads 258K. TikTok 147K. 4 things that did the compounding: 1. One platform at a time, go deep 2. Voice memos over writer's block 3. Waterfalling winners every 90 days 4. A newsletter that owns the relationship Slow is smooth. Smooth is fast.view source ↗
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Unstoppable.⚡ 🎮 Recreated in @play_eFootball https://t.co/6lwwsBsx7h Unstoppable.⚡ 🎮 Recreated in @play_eFootball https://t.co/6lwwsBsx7hview source ↗
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Imparable. ⚡ 🎮 Recreado en @play_eFootball https://t.co/xcBRUomSP0 Imparable. ⚡ 🎮 Recreado en @play_eFootball https://t.co/xcBRUomSP0view source ↗
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Imparable. ⚡ 🎮 Recreado en @play_eFootball https://t.co/RLFHsFWxyA Imparable. ⚡ 🎮 Recreado en @play_eFootball https://t.co/RLFHsFWxyAview source ↗
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Fire, smoke seen near Aramco facility in Riyadh, witness says https://t.co/X550RzVTw7 https://t.co/X550RzVTw7 Fire, smoke seen near Aramco facility in Riyadh, witness says https://t.co/X550RzVTw7 https://t.co/X550RzVTw7view source ↗
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If I had 6 months to become an AI Evals Engineer. I’d do this. Stage 1: Testing Foundations and Statistics for AI - Learn: pytest (fixtures, parametrize), JSONL pipelines, precision/recall/F1, confidence intervals, Cohen's kappa. - Practice: Build a pytest plugin that loads a JSONL eval dataset and emits a metric r… If I had 6 months to become an AI Evals Engineer. I’d do this. Stage 1: Testing Foundations and Statistics for AI - Learn: pytest (fixtures, parametrize), JSONL pipelines, precision/recall/F1, confidence intervals, Cohen's kappa. - Practice: Build a pytest plugin that loads a JSONL eval dataset and emits a metric report on every run. - Why: Evals are tests and tests are math. If you cannot quantify agreement, you cannot prove improvement. Stage 2: LLM Failure Modes and Behavior - Learn: temperature and top-p sampling, seed control, tokenization edge cases, instruction drift, hallucination taxonomies. - Practice: Fuzz one model with 500 adversarial inputs and classify every failure into a taxonomy with reproduction steps. - Why: Every eval suite is a map of known failure modes. You cannot evaluate what you do not understand. Stage 3: Golden Dataset Engineering - Learn: annotation guidelines, stratified sampling, inter-annotator agreement, dataset versioning, contamination detection. - Practice: Build a 300-case golden dataset with 2 annotators, measure kappa, and version it in git with a changelog. - Why: Your evals are only as good as your dataset. Garbage labels produce garbage confidence. Stage 4: Scoring Methods and LLM-as-a-Judge - Learn: exact and fuzzy match, embedding similarity, rubric design, judge model selection, judge biases (verbosity, position, self-preference). - Practice: Build an LLM-as-a-judge with a rubric, calibrate it against 100 human labels and publish the agreement score. - Why: Judges drift and lie. Calibrated judges are instruments; uncalibrated ones are vibes. Stage 5: RAG Evaluation - Learn: hit rate, MRR, NDCG, recall@k, faithfulness vs relevance, citation grounding, abstention quality. - Practice: Build a RAG eval harness with separate retrieval and generation gates, plus adversarial queries that must return "no answer". - Why: RAG fails in two places. A single score hides which one is broken. Stage 6: Ageview source ↗
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Looking for new startups. Drop your link. Looking for new startups. Drop your link.view source ↗
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TradingView doesn't want you to know about this A group of devs reverse-engineered TradingView Premium using Claude Fable 5.1 and put it out on GitHub completely free The GitHub repo just hit 42.1k stars, and they already have 25k members on their Discord It's currently available as a standalone app for Windows and … TradingView doesn't want you to know about this A group of devs reverse-engineered TradingView Premium using Claude Fable 5.1 and put it out on GitHub completely free The GitHub repo just hit 42.1k stars, and they already have 25k members on their Discord It's currently available as a standalone app for Windows and Mac, with GitHub and Discord links available inside the appview source ↗