Harwin Prahara

28.1K posts

Harwin Prahara

Harwin Prahara

@hardwinder

Engineer @cookpad_dev

🇬🇧 Katılım Mayıs 2010
577 Takip Edilen392 Takipçiler
Harwin Prahara retweetledi
Harwin Prahara retweetledi
Como1907
Como1907@Como_1907·
Como 1907 is deeply saddened by the passing of Michael Bambang Hartono. We extend our sincere condolences to the Hartono family and to all at the Djarum Group. Under the family’s leadership, the Club has entered a new chapter in its history, and we remember him with gratitude and respect.
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Stitch by Google
Stitch by Google@stitchbygoogle·
Meet the new Stitch, your vibe design partner. Here are 5 major upgrades to help you create, iterate and collaborate: 🎨 AI-Native Canvas 🧠 Smarter Design Agent 🎙️ Voice ⚡️ Instant Prototypes 📐 Design Systems and DESIGN.md Rolling out now. Details and product walkthrough video in 🧵
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Pokémon UK
Pokémon UK@PokemonNewsUK·
Whether you joined us in Pallet Town or the Paldea region, thank you for being with us on this adventure to catch 'em all! 🙌⚡ Share your best Pokémon memories in the comments ⬇️ #Pokemon30
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BBC News (UK)
BBC News (UK)@BBCNews·
Pokémon at 30: Fans explain what the series means to them bbc.in/4lhvwbf
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Docker
Docker@Docker·
Running OpenClaw locally? Do it safely. This walkthrough shows how to run it inside Docker Sandboxes with Docker Model Runner: - Isolated microVM - No exposed API keys - Controlled network access - Fully private, local AI setup Secure agent workflows in ~2 commands. Read → bit.ly/4sgSKAy
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Harwin Prahara
Harwin Prahara@hardwinder·
100 PRs/hour!
DHH@dhh

@karpathy The early AI made Omarchy possible simply by teaching me core Linux concepts at an accelerated pace and decoding error messages, but with today's agents I was able to process 100 PRs in an hour the other night. Completely crazy acceleration!

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Harwin Prahara retweetledi
Andrej Karpathy
Andrej Karpathy@karpathy·
It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow. Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes. As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now. It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.
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ポケモン公式
ポケモン公式@Pokemon_cojp·
30周年を記念した特別な映像を公開! 『ポケットモンスター 赤・緑』の発売から30年。 2026年2月27日(金)にポケモンは30周年を迎えたよ。 これまでも、これからも、キミといっしょに冒険を続けていくポケモンたちの映像を楽しんでね。 #ポケモン30周年 #ポケモンプレゼンツ
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Pokémon
Pokémon@Pokemon·
If you recognize this, it’s time for your Potion
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Pokémon
Pokémon@Pokemon·
════════════ Pokémon FireRed and Pokémon LeafGreen confirmed for Nintendo Switch! ═══════════ These download-exclusive titles will be available after the #PokemonDay Presents presentation which begins Friday, February 27, 2026, at 6AM PST. #PokemonFRLG
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Yichao 'Peak' Ji
Yichao 'Peak' Ji@peakji·
Well, I think I need to clarify this: Manus personal agent mode is not based on @openclaw. It is still built entirely on Manus's in-house architecture. That said, I still believe it's necessary to pay respect to the OpenClaw project, because it further experimented with and helped shape the "safety boundary" between users and agents. Exactly one year ago today, we were doing the final polishing before Manus's release. The problem that troubled us the most at the time was actually: How do you make users trust an agent? This isn't about the source code - it's about the uncertainty that comes with limitless possibility. Back then, non-technical users, and really the entire world, had no concrete experience with AI agents. User education therefore fell onto a small startup. 😂 To address this, we designed four things: * Cloud Sandbox: A fully isolated virtual machine is launched for each task so it cannot affect the user's own computer. * Task Replay: You can replay tasks shared by others in read-only mode to safely explore what an agent is. * Interruptible Action Chain: You can inspect the agent's step-by-step actions and interrupt or correct them on the fly. * GUI Takeover: Whenever login or human intervention is required, the agent requests the user to take control of the browser, mouse, and keyboard. These safety measures accompanied Manus through its first year. They allowed millions of users to experience a general agent for the first time, and eventually became standard features across agent products. Times have changed. As agents complete tasks more reliably, run faster, models grow smarter, and users become more familiar with them, we believe it's time to move from "building trust" toward "lowering the barrier". We are now doing this by: * Reusing Manus's mature virtualization layer so users can use it without configuration. * Introducing a new context-management system so users won't need to switch sessions in messengers. * Using intelligent subagent scheduling to parallelize tasks while balancing speed and quality. * Replacing more slash commands and menus with natural language. This strongly aligns with our long-standing mission: To extend human reach. To put the full power of AI to work by unlocking the code, not just for engineers but for everyone.
Manus@ManusAI

Introducing Manus Agents — your personal Manus, now inside your chats. 👉🏻Long-term memory. Remembers your style, tone, and preferences. 👉🏻Full Manus power. Create videos, slides, websites, images from one message. 👉🏻Your tools, connected. Gmail, Calendar, Notion, and more. Available now on Telegram. More platforms coming soon.

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Harwin Prahara
Harwin Prahara@hardwinder·
Happy Pancake Day 🥞 & Chinese New Year 🔥🐴🧧! Gong xi fa cai!
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Manus
Manus@ManusAI·
Introducing Manus Agents — your personal Manus, now inside your chats. 👉🏻Long-term memory. Remembers your style, tone, and preferences. 👉🏻Full Manus power. Create videos, slides, websites, images from one message. 👉🏻Your tools, connected. Gmail, Calendar, Notion, and more. Available now on Telegram. More platforms coming soon.
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