Simon L

1.1K posts

Simon L

Simon L

@_dillik_

Engineer pretending to be a manager.

Katılım Kasım 2011
931 Takip Edilen35 Takipçiler
Simon L
Simon L@_dillik_·
muxy is a breeze
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联合早报 Lianhe Zaobao
一名随同美国总统特朗普访华的美国记者称,他们登上总统专机“空军一号”离开北京返回美国前,美方人员收走了中国官员分发的所有东西,并扔进垃圾桶,“飞机上禁止携带任何来自中国的物品”。 #Echobox=1778841177" target="_blank" rel="nofollow noopener">zaobao.com.sg/news/china/sto…
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Allie K. Miller
Allie K. Miller@alliekmiller·
The most expensive mistake in enterprise AI right now: treating FDEs as your whole transformation plan. Forward deployed engineers (FDEs) are important for custom deployments, but they won’t fix the change management issue most enterprises are facing. It’s likely more the former that Anthropic and OpenAI will continue to prioritize (and hire into the thousands, who knows). Beyond performance and cost, it’s systems integration, ROI, and literal usefulness that drive revenue and stickiness. *However* External FDEs, in my opinion, will not make your company an AI-first company. You can have the sleekest multi-agent orchestrations and still have the majority of your employee base hating AI, avoiding AI, and distrusting leadership decisions on AI. And we already know this because we see this in traditional SaaS too: you can customize the heck out of your Salesforce deployment, but that doesn’t mean your sales team will improve their data hygiene or even attempt to change the way they track and grow with it. Buying a fancier car doesn’t mean you magically learn to drive better overnight. If you’re an enterprise exec and FDEs are sold as the immediate and sole solution to your company transformation woes, walk away. It’s the combination of tech *and* people enablement *and* process reinvention that compounds into actual business outcomes. Large complex enterprises will stall out if they only prioritize the first.
Aaron Levie@levie

Forward deployed engineers, or equivalent, are about to become one of the most in-demand jobs in tech. And one of the most important functions for AI rollouts. Deploying agents is far more technical of a task than most people realize, often far more involved than deploying software. Software generally works the same way every time, and generally for the past few decades has been updated versions of an existing technology or concept (which basically means easier for the enterprise to update their workflows on a newer system). With agents, you’re actually deploying the equivalent of work output within the enterprise. The customer is effectively using you as a professional services provider for a task, which they expect to get solved nearly end-to-end now. This means you need to actually deeply understand the business process as a vendor, and get the customer from the current to the end state seamlessly. Companies need help figuring out which models will work best for their workflows, they need extensive evals setup often, they need change management support for workflows, they need to get their data setup for the agents, and constant tuning of the agentic system for their process. Massive role in tech now. And another example of the kind of highly technical work that AI is creating.

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Peter Steinberger 🦞
Peter Steinberger 🦞@steipete·
Kudos to Microsoft, they're helping to get OpenClaw ready for enterprises.
Omar Shahine@OmarShahine

New in @openclaw beta: one path scheme to rule them all. `openclaw path read|write|append` works the same across md, jsonc, jsonl, and yaml — plugins and agents now share a single addressing substrate for surgical edits to structured files. Nice work Gio Della-Libera — one of many PRs from the Microsoft Project Lobster maintainer crew! github.com/openclaw/openc…

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Pixel Updates
Pixel Updates@pixel_updates·
Google announced the Googlebook, coming later this year.
Pixel Updates tweet mediaPixel Updates tweet media
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Simon L
Simon L@_dillik_·
@esrtweet if rust is readable, which language isn't?
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Eric S. Raymond
Eric S. Raymond@esrtweet·
Choice of language for software projects has become a very different game now that we have robot friends to do most of our code generation and translation for us. I have people wondering why I just shipped a project in Rust when I don't like the language and don't hand-code in it myself. I did this because I am adjusted to current reality, and now I'm going to talk about that. The age of hand-coding is mostly over. It no longer matters as much whether the computer language I use is comfortable to my hand, only whether the robot friend I'm using can generate it at high quality. It also matters whether I can read the language, because I am going to want to run my eyeball over it to review the code. Rust meets that bar - I find it kind of spiky but basically readable. Rust is a good deployment language for me to choose when (a) I want solid memory-safety guarantees, and (b) the code is already mature and I don't expect to need to do exploratory programming or serious feature development on it in the future. In particular, this makes Rust a good place for me to land my old C projects. Which is why in the last couple of months I have migrated two of them to Rust. C to Rust translation by robots is cheap and easy now; I will probably continue to do this. Each time I get a bug report on one of these projects in the future, boing! Rusticated. You may believe that Rustacea is stuffed with Communists and sexual deviants. You might even be right. I don't have to care whether that's true anymore, because I have a robot friend who is in all relevant ways smarter than they are. The wider lesson here is that the developer and user community around a language doesn't matter as much as it used to in whether you should get involved with it. Because in the future, we're going to be relying on human community brains less and artificial intelligences more. And that future is now. Not everything C gets moved to Rust, though. I lifted cvs-fast-export to Golang instead, because I think it's fairly likely that I'm going to have to do significant development work on it is in the future, so the payoff from a language I'm more comfortable reading and modifying by hand goes up. I'm certainly never going to start a project in C again. What would be the point, other than masochism? I spent 40 years writing C and I'm very good at it, but I will cheerfully leave it and it's buffer overruns and its heap corruption and its undefined behaviors and its portability problems behind. It helps that my robot friends are good at writing C code that doesn't have those problems, but...why even go there? Why expose yourself to those risks if the robot misses something? These days I do my exploratory programming in Python or Golang. My robot friends are extremely good at generating code in both those languages. I think they're slightly higher leverage on Golang, possibly due to that language having a smaller surface? Python used to be my favorite language. I soured on it for a while after the 2-to-3 transition was massively botched, and the GIL meant concurrency in it was a disaster area, and managing library dependencies became an even bigger disaster area. I'm a little happier with Python now that I can declare strict typing and uv has reduced dependency pain somewhat. But I think if I think I'm going to have to write anything much larger than a glue script in Python, I just shrug and reach for Golang instead. I'm very comfortable in Golang. Over time, I'll probably migrate my older Python projects to Golang because that's cheap and easy now and the performance win can be quite significant. I don't know what other languages I'm going to be using in the future. I do know that choosing a development language is a much less grave commitment than it used to be, because if it turns out to be not well suited for the job I'm doing, I can simply have my robot friend translated to a better one.
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OpenAI
OpenAI@OpenAI·
Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI. It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies, and system integrators to help organizations deploy frontier AI to production for business impact. openai.com/index/openai-l…
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Car Grails
Car Grails@GrailArchive·
530 horsepower. LiDAR. 11 cameras😱. 12 ultrasonic sensors. The Aito M9 isn’t just a luxury SUV it’s a technology statement on wheels. Huawei cooked here
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Claude Code Changelog
Claude Code Changelog@ClaudeCodeLog·
Claude Code 2.1.139 has been released. 50 CLI changes, 1 system prompt change Highlights: • Added /goal command to run tasks across turns until a set completion condition, with live elapsed/turns/tokens • System prompt compaction is now silent (no trimming alert), which may obscure that prompts were trimmed • Compaction prompt asks model to preserve sensitive instructions, reducing loss of user intent during trimming Full details are in thread ↓
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Haider.
Haider.@haider1·
bad news LLMs are starting to look like they're hitting a wall
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@levelsio
@levelsio@levelsio·
@gossipaddress You can do 72,000 serial writes per second though with a properly configured SQLite install
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Simon L
Simon L@_dillik_·
@mr_r0b0t how does one use it with claude code
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mr-r0b0t
mr-r0b0t@mr_r0b0t·
Be me: Sign up for DeepSeek API because everyone is saying how reasonably priced it is. Load up $50, let’s see. 30 minutes in, here’s where we’re at using V4-Pro
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andrew engler
andrew engler@aerockrose·
Yesterday, Andrej Karpathy gave a 30-minute Sequoia masterclass on agentic engineering. This is the serious layer above vibe coding. He explained: - LLMs as ghosts - The app that shouldn't exist - Outsource thinking, not understanding 12 lessons that will blow your mind: 🧵
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Simon L
Simon L@_dillik_·
@Snooker_Chat xintong seems to be a nice kid. i am ok with the transition
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Snooker Chat 🔴⚫🔴
Snooker Chat 🔴⚫🔴@Snooker_Chat·
Zhao Xintong said he did not enjoy his second round battle with Ding Junhui despite being the winner. Zhao is a step closer to defending his world crown after beating his hero 13-9. He said: "We knew everyone had their eyes on us. I couldn't enjoy it." #snooker Pic: BBC
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WST
WST@WeAreWST·
A "world class" Ding shot helps him draw level at 4-4 with the defending champ #worldchampionship
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Global Statistics
Global Statistics@Globalstats11·
Popular Brands You Didn't Know Are From Asia 1. 🇹🇼 Taiwan: 💻 Acer 💻 Asus 2. 🇸🇬 Singapore: 🥐 BreadTalk 🎮 Razer 3. 🇨🇳 China: 🛍️ Miniso 📱 OnePlus 🧊 Haier 👗 Shein 💻 Lenovo 4. 🇮🇳 India: 🎨 Asian Paints 🚿 Jaquar 💻 Infosys 5. 🇰🇷 South Korea: 🍱 LocknLock 👟 Fila 6. 🇯🇵 Japan: 👕 Uniqlo 🛞 Bridgestone 📷 Canon 7. 🇵🇭 Philippines: 🍗 Jollibee 8. 🇮🇩 Indonesia: ☕ Kopiko 🍜 Indomie 9. 🇹🇭 Thailand: ⚡ Red Bull (Thai Origin Drink) 🍺 Chang 10. 🇲🇾 Malaysia: ✈️ AirAsia 🍰 Secret Recipe ☕ San Francisco Coffee 11. 🇳🇵 Nepal: 🍜 Wai Wai 12. 🇦🇪 UAE: ✈️ Emirates 13. 🇻🇳 Vietnam: 🚗 VinFast ☕ Trung Nguyen 14. 🇱🇰 Sri Lanka: 🍵 Dilmah
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