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claude / pricing
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Showing 1126–1150 of 1178 mentions
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I usually write for developers but I'm aiming my next post at leaders of companies trying to navigate the change of AI coding agents what's something you wish your leadership would get about agents? or if you're running a company- what problems are you running into? I usually write for developers but I'm aiming my next post at leaders of companies trying to navigate the change of AI coding agents what's something you wish your leadership would get about agents? or if you're running a company- what problems are you running into?view source ↗
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Delivering Zomato orders in Indiranagar tonight!!! One guy tipped us ₹200 😭 Book an order now, our paths may cross :p https://t.co/eIAJb054iC Delivering Zomato orders in Indiranagar tonight!!! One guy tipped us ₹200 😭 Book an order now, our paths may cross :p https://t.co/eIAJb054iCview source ↗
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🚨BREAKING: SoftBank has officially wired its final $10 BILLION into OpenAI Masa had to sell his ENTIRE Nvidia stake to fund the initial payment and was so leveraged it had to issue an $11.1B junk bond to wire this final check SoftBank now owns ~13% of OpenAI https://t.co/DBtqvOroXH 🚨BREAKING: SoftBank has officially wired its final $10 BILLION into OpenAI Masa had to sell his ENTIRE Nvidia stake to fund the initial payment and was so leveraged it had to issue an $11.1B junk bond to wire this final check SoftBank now owns ~13% of OpenAI https://t.co/DBtqvOroXHview source ↗
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I’d be issuing an apology to my users if a one person PlanetScale side project caused me to realize my product was slow as fuck and all it took to fix was two weeks of work. Humiliating really. I’d be issuing an apology to my users if a one person PlanetScale side project caused me to realize my product was slow as fuck and all it took to fix was two weeks of work. Humiliating really.view source ↗
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This is why I have a Tesla Y One of the safest car brands and car models I don't trust myself nor anyone else driving so when shit goes down I wanna be in not the prettiest or coolest car, I wanna be in the safest car with the highest survival rate Which all the recent Tesla models are This is why I have a Tesla Y One of the safest car brands and car models I don't trust myself nor anyone else driving so when shit goes down I wanna be in not the prettiest or coolest car, I wanna be in the safest car with the highest survival rate Which all the recent Tesla models areview source ↗
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winter arc day 1 >joined gym after lot of thinking ( coz every membership i have taken till date has been waste😭) >set up repo locally , read through loads of documentations https://t.co/sgR6Rr20sg winter arc day 1 >joined gym after lot of thinking ( coz every membership i have taken till date has been waste😭) >set up repo locally , read through loads of documentations https://t.co/sgR6Rr20sgview source ↗
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"Hey Codex, show me how you'd fix this bug" The new tldraw plugin lets ChatGPT map a problem on the canvas and sketch a few fixes. Comment on the drawing to ask for changes and watch it update live. Here's Max trying it on one of our bugs: https://t.co/OJzLgZtuTC "Hey Codex, show me how you'd fix this bug" The new tldraw plugin lets ChatGPT map a problem on the canvas and sketch a few fixes. Comment on the drawing to ask for changes and watch it update live. Here's Max trying it on one of our bugs: https://t.co/OJzLgZtuTCview source ↗
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Also, 6.1 Sol was our fastest-growing model ever, and was a bit slow under load. Should be much better now! Also, 6.1 Sol was our fastest-growing model ever, and was a bit slow under load. Should be much better now!view source ↗
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RT @daxeshpatel: Google's ADK used to pass an agent eval that ran zero cases. 2.10 now errors. Pipelines will go red on upgrade. Don't pin… RT @daxeshpatel: Google's ADK used to pass an agent eval that ran zero cases. 2.10 now errors. Pipelines will go red on upgrade. Don't pin…view source ↗
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.@clairevo: "We can now ship all our bad ideas. Congratulations!" https://t.co/1Cg4a5D545 .@clairevo: "We can now ship all our bad ideas. Congratulations!" https://t.co/1Cg4a5D545view source ↗
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@irvinebroque @ghostwriternr THIS SOLVES SO MANY PROBLEMS FOR US @irvinebroque @ghostwriternr THIS SOLVES SO MANY PROBLEMS FOR USview source ↗
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Day 274 of road to $30k/mo Only 3 months left in 2026… This year changed my life I went from: - mowing lawns for money - building my first app - failing completely (after months of building 😭) - trying again - scaling my 2nd app to $29k rev in last month All while documenting every single day (which in itself was… Day 274 of road to $30k/mo Only 3 months left in 2026… This year changed my life I went from: - mowing lawns for money - building my first app - failing completely (after months of building 😭) - trying again - scaling my 2nd app to $29k rev in last month All while documenting every single day (which in itself was extremely uncomfortable for me) You can literally change who you are in just a couple of months Don’t give up!view source ↗
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The current situation of developers 💀😭 https://t.co/WX66DoXxXo The current situation of developers 💀😭 https://t.co/WX66DoXxXoview source ↗
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Rough tier list of free github skills i'd actually install in 2026 https://t.co/X2TLYvVd2o Rough tier list of free github skills i'd actually install in 2026 https://t.co/X2TLYvVd2oview source ↗
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atp people are building an ai solution to the tiniest of problems at the speed of light atp people are building an ai solution to the tiniest of problems at the speed of lightview source ↗
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As a Backend Engineer in the AI era, you must build these projects. 1.) High-Concurrency HTTP Server Build: Go or Rust server handling 10k+ concurrent connections with graceful shutdown and backpressure. Why: Concurrency architecture is the one thing AI cannot design for you. 2.) Multi-Tenant Hybrid Search Databas… As a Backend Engineer in the AI era, you must build these projects. 1.) High-Concurrency HTTP Server Build: Go or Rust server handling 10k+ concurrent connections with graceful shutdown and backpressure. Why: Concurrency architecture is the one thing AI cannot design for you. 2.) Multi-Tenant Hybrid Search Database Build: Postgres with Row-Level Security + pgvector + BM25 hybrid ranking across isolated tenants. Why: Structured data and semantic embeddings now share one query engine. 3.) Event-Sourced Order Pipeline Build: Kafka or NATS pipeline with immutable events, dead-letter queues, exactly-once semantics. Why: AI workloads are slow and async. The main thread must never block. 4.) Durable Onboarding Workflow Build: 3-day Temporal workflow with checkpoints, retries and human approval steps. Why: Background jobs are for emails. Durable execution is for multi-step agents. 5.) AI Gateway with Semantic Cache Build: Proxy that caches similar prompts via embeddings, enforces token budgets and fails over to a cheaper model on 503s. Why: The gateway protects your margins and your uptime from flaky, expensive LLM APIs. 6.) PII-Redacting Auth Middleware Build: OAuth2/OIDC provider plus middleware that masks PII before logging and restricts AI agents to read-only DB roles. Why: When agents can write to your database, a prompt injection is a data breach. 7.) Real-Time Streaming Dashboard Build: SSE/WebSocket dashboard streaming LLM tokens and database updates simultaneously with backpressure. Why: Time-To-First-Token and perceived latency are the new UX standards. 8.) Correlated Tracing Pipeline Build: OpenTelemetry + Langfuse pipeline linking each HTTP request to the exact LLM prompt and DB query it triggered. Why: You cannot debug a hallucination without replaying the exact context the model saw. 9.) Ephemeral Environment Provisioner Build: Terraform or Pulumi scripts spinning up a complete isolated staging environment for every pull requeview source ↗
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Pewdiepie casually explained why you should learn coding better than Indian influencers whose entire career is teaching coding😭😭 https://t.co/Y0VueT9F2b Pewdiepie casually explained why you should learn coding better than Indian influencers whose entire career is teaching coding😭😭 https://t.co/Y0VueT9F2bview source ↗
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Working in tech means fumbling generational wealth every few years 😭 https://t.co/IrbuzVVLDr Working in tech means fumbling generational wealth every few years 😭 https://t.co/IrbuzVVLDrview source ↗
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@IbrahimElkamali Holy shit this is so bad. I’m 3 minutes in and everything he’s saying I’m doing “wrong” is how having a real job worked, even before AI. @IbrahimElkamali Holy shit this is so bad. I’m 3 minutes in and everything he’s saying I’m doing “wrong” is how having a real job worked, even before AI.view source ↗
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what’s happening to chief ai officers 😭 https://t.co/VdlzLHY6Tx what’s happening to chief ai officers 😭 https://t.co/VdlzLHY6Txview source ↗
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𝗖𝗹𝗮𝘂𝗱𝗲 𝗢𝗽𝘂𝘀 𝟱.𝟱 𝗜𝘀 𝗡𝗼𝘄 𝗟𝗶𝘃𝗲 𝗼𝗻 𝗕.𝗔𝗜 ⚡ https://t.co/9CKQIEP2Jz has added Claude Opus 5.5, the first model in Anthropic’s new Claude 5.5 family, to both its API and Web Chat. This model is built around a specific class of workloads: long-running Agent tasks and complex knowledge work. 🧠 Built For Long-Running Agents … 𝗖𝗹𝗮𝘂𝗱𝗲 𝗢𝗽𝘂𝘀 𝟱.𝟱 𝗜𝘀 𝗡𝗼𝘄 𝗟𝗶𝘃𝗲 𝗼𝗻 𝗕.𝗔𝗜 ⚡ https://t.co/9CKQIEP2Jz has added Claude Opus 5.5, the first model in Anthropic’s new Claude 5.5 family, to both its API and Web Chat. This model is built around a specific class of workloads: long-running Agent tasks and complex knowledge work. 🧠 Built For Long-Running Agents Claude Opus 5.5 is designed for workflows where an AI needs to work through a problem over multiple steps rather than simply answer a single prompt. Its capabilities are particularly relevant to: 🔹 Autonomous coding workflows 🔹 Complex software engineering 🔹 Long-running Agent tasks 🔹 Research and knowledge work 🔹 Computer Use It supports a 1M-token context window and up to 128K output tokens, giving developers substantial room for large codebases, documents and multi-step tasks. ⚡ More Capability, Lower Running Cost According to the supplied announcement, Opus 5.5 performs around the level of Claude Fable 5.1 on most tasks, while costing approximately 40% less to run than Opus 5. That matters for Agentic workloads. When an Agent performs dozens—or potentially hundreds—of model calls during a workflow, inference cost becomes part of the architecture. Lower cost can therefore make longer-running workflows more practical. 🔌 Now Available Through https://t.co/9CKQIEP2Jz The bigger https://t.co/9CKQIEP2Jz story is the accessibility layer. Developers can access Claude Opus 5.5 through the https://t.co/9CKQIEP2Jz API, while regular users can also interact with it through https://t.co/9CKQIEP2Jz Web Chat. That means the same model can fit into both: Developer workflows → API → applications & Agents and Everyday workflows → Web Chat → research & knowledge work AI infrastructure is increasingly becoming about more than individual models. It is also about how easily developers can access, compare and integrate different models into the workflows they already use. 👉 Try Claude Opus 5.5: https://t.co/IE5evqOJKL 🔗 Learn more: https://t.co/49pKwxSview source ↗
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There will be MANY vulnerable moments between now and my October 8 “would have been” milestone anniversary.💔 https://t.co/4b8oxLghDg There will be MANY vulnerable moments between now and my October 8 “would have been” milestone anniversary.💔 https://t.co/4b8oxLghDgview source ↗
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@MadisonRaeGun seeing both her and madelyn cline in this is very disappointing @MadisonRaeGun seeing both her and madelyn cline in this is very disappointingview source ↗
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@Stefan_3D_AI Interesting that Opus 5.5 can’t do geometry node hair in blender - it’s really really bad at it in all my many tests. The vision model is just not good, that’s truly the limiting part. It “sees” in very low res. It also can’t problem solve hair guides because it can’t get its head around human hair “rul… @Stefan_3D_AI Interesting that Opus 5.5 can’t do geometry node hair in blender - it’s really really bad at it in all my many tests. The vision model is just not good, that’s truly the limiting part. It “sees” in very low res. It also can’t problem solve hair guides because it can’t get its head around human hair “rules”. I also think there’s just not enough training material (ie. Very few finished ultra realistic hairstyles out there as .blend files, aside from the official demo project). And also, there’s very little documentation and the tutorials out there are reliant on an individual’s artistic merit (ie. They aren’t easily reduced to “formula”)view source ↗
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【今日のAI今北産業｜0285:Agents' Last Exam】 AI Agentを「試験問題」ではなく、本物の仕事で測るBenchmarkへ 55分野・13産業Cluster、長時間の実務WorkflowをSandbox上で最後まで実行 高得点Benchmarkと「本当に仕事を任せられるか」のGapを測る ← イマココ Agents' Last Exam。 略してALE。 名前だけ聞くと、 「AI Agent向けの難しい試験」 に見えます。 でも本質は少し違います。 ALEが測ろうとしているのは、 AIが難しいQuestionに答えられるか ではありません。 現実の仕事を渡した時に、 必要なSoftwareを使… 【今日のAI今北産業｜0285:Agents' Last Exam】 AI Agentを「試験問題」ではなく、本物の仕事で測るBenchmarkへ 55分野・13産業Cluster、長時間の実務WorkflowをSandbox上で最後まで実行 高得点Benchmarkと「本当に仕事を任せられるか」のGapを測る ← イマココ Agents' Last Exam。 略してALE。 名前だけ聞くと、 「AI Agent向けの難しい試験」 に見えます。 でも本質は少し違います。 ALEが測ろうとしているのは、 AIが難しいQuestionに答えられるか ではありません。 現実の仕事を渡した時に、 必要なSoftwareを使い、 Fileを扱い、 調査し、 判断し、 何度も操作し、 成果物を作り、 条件を確認し、 最後まで本当に仕事を終えられるか。 です。 2026年6月、UC Berkeley RDIを中心とする研究チームがAgents' Last Examを公開しました。 Paperの問題意識はかなり明確です。 AIはさまざまなBenchmarkで急速に高得点を取るようになった。 それなのに、 多くのProfessional Domainでは、 そのBenchmark性能が同じ速度でEconomically MeaningfulなDeploymentへ変わっているわけではない。 なぜか。 研究チームは、 そのGapの大きな原因の一つが「Evaluation」にあると考えました。 つまり、 測っているものが実際の仕事と違う。 そこで作ったのがALEです。 ■従来Benchmarkと何が違うのか 従来のBenchmarkには大きな価値があります。 数学問題を解く。 知識Questionへ答える。 Codeを書く。 Bugを直す。 Browserで操作する。 Terminal Taskを処理する。 AI Capabilityの進歩を見るには必要です。 ただし現実の仕事は、 1問答えて終わり ではありません。 例えばProfessional Workでは、 依頼内容を理解する。 Input Fileを確認する。 必要なDataを探す。 Softwareを立ち上げる。 複数Fileを編集する。 途中結果を検証する。 間違っていたら戻る。 別Toolへ移る。 最終Deliverableを指定形式で保存する。 という長いWorkflowになります。 ALEは、 このLong-horizon Workを測ります。 ■問題ではなく「Project」を渡す ALEのTaskは、 SyntheticなPuzzleだけを作るのではなく、 Industry Expertが実際に経験・完了したProfessional Workflowをもとに設計されます。 Paperでは250人超のIndustry Expertと開発。 現在の公式Framework Documentationでは、 55 Subdomain、 13 Industry Cluster、 1,000件超のTask Pool、 約150件のPublic Release と説明されています。 公開時のBerkeley RDI Blogでは、 1,500件超のExpert-sourced Taskとして紹介されていました。 数字が違って見えるのは、 ALEが固定Benchmarkではなく、 Task Poolが継続的に増減・更新されるLiving Benchmarkとして設計されているためです。 重要なのは、 一つのCoding領域だけではないこと。 Computing。 Engineering。 Science。 Health。 Business。 Finance。 Law。 Education。 Visual／Media系など、 幅広いNon-physical Professional Workを扱います。 分類には米国のO*NET／SOC 2018 Occupational Taxonomyを参照しています。 つまり、 「AI AgentはSoftware Engineerの仕事ができるか」 だけではなく、 Professional Digital Work全体でどこまで働けるか を測ろうとしています。 ■GUIもCLIも使う 現実の仕事では、 Terminalだけ使うわけではありません。 Browser。 Office Software。 Specialized Application。 GUI。 CLI。 File System。 Web。 全部を行き来します。 ALEではReproducible Dview source ↗