ogx

956 posts

ogx

ogx

@ogx5125

Katılım Şubat 2024
767 Takip Edilen50 Takipçiler
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Garry Tan
Garry Tan@garrytan·
Realization: in the past, we wrote code to call LLMs Today, we write prompts and skill files for LLMs to execute code. Tomorrow? Yet unwritten. We will find out soon.
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plantegg
plantegg@plantegg·
中年码农失业 今天在论坛看到一个帖子,一个38岁的程序员被裁了。不是那种混日子的,是正经大厂,干了十年,带过团队,年年绩效B+以上。 HR找他谈的时候说的是"业务调整",但他心里清楚,组里新来的那批校招生,工资是他的三分之一,代码写得也不差。他不是被淘汰的,他是被"性价比"掉的。 最让我觉得残酷的是评论区。一堆人在说"早该转型了""35岁还不做管理就是自己的问题"。好像一个人兢兢业业写了十年代码,是一种罪过。好像技术本身不值钱,值钱的只有你愿意加班到几点、你的年龄数字够不够小。 我就想问一句:一个行业,如果干到38岁就算"老"了,如果十年经验反而成了负资产——这到底是个人的失败,还是整个系统在用人当耗材? 中国互联网最大的谎言就是"技术改变世界"。技术没改变什么,它只是把一代人的青春榨干,然后换一批更便宜的继续榨。
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UTM
UTM@UTMapp·
We started a development blog! First post: bringing DirectX support to QEMU with the help of AI. blog.getutm.app/2026/introduci…
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Tech Fusionist
Tech Fusionist@techyoutbe·
One message to your manager, when they call you over weekend for unplanned work?
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ogx
ogx@ogx5125·
@Barret_China 代码只是基本能力,每个人都可以掌握
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Barret李靖
Barret李靖@Barret_China·
大多数程序员会慢慢出现两类症状。 一是不想写代码。因为写的没有 AI 快,也写的没有 AI 好,编程将沦为普通人的玩具(架构和工程目前还不是),程序员容易找不到成就感。 二是不想指挥 AI 写代码。由于一的存在,加上指挥一个没有感情的机器,去做自己不认可的事情,会比自己亲自去做更难受。因为躬身事中,疲于处理一个又一个的问题,会忘记自己的存在;而看着 AI 在自己不认可的方向,持续接近目标,会无数次巩固自己的存在。“我是谁?我在干啥?” 程序员应该做的,就是摆脱自己会写代码的标签,慢慢朝着用户价值层做迁移,去做自己认可的、喜欢的事情,或者用自己认可的、喜欢的方式做事情。😃
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ogx
ogx@ogx5125·
@vlad_mihalcea AI不会影响你的职业生涯,其他人会
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Vlad Mihalcea
Vlad Mihalcea@vlad_mihalcea·
As a software developer, it's very important to know Galton’s Law of Mediocrity (Regression Towards the Mean) in the context of AI. According to Galton, as long as you are better than the average or have skills that AI cannot replicate (e.g., creativity, high agency, determination, communication, and teamwork), you are going to be fine in your career.
Vlad Mihalcea tweet media
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avrl ☘
avrl ☘@avrldotdev·
non-tech people who enjoy software engineers going out of jobs, you'll be next after all of us are unemployed.
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ogx
ogx@ogx5125·
@sudoingX Cool ,有实际参考意义
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Sudo su
Sudo su@sudoingX·
anyone thinking about, learning, or already working with agentic systems, you should know this. the first few steps of your setup matter more than any model or framework you pick later. get them right and you never lose your flow. the foundation nobody posts about: > 1. tailscale. a private mesh network across every machine you own. laptop, desktop, rented node, all on one secure tailnet, reachable from anywhere. nothing else works well until this does. > 2. termius, over that tailnet. one SSH client that reaches every node, phone included. you are never away from your stack. > 3. tmux. persistent sessions. disconnect, close the laptop, come back, every session exactly where you left it. agentic work runs long, your terminal has to survive that. > 4. a private git repo. the one i am most glad i found. it is the memory layer across all my agents, they pull, they work, they merge back, the codebase stays alive between sessions. context that would die in a chat window lives in the repo instead. > 5. script everything from day one. ssh aliases for every node, setup scripts, the boring boilerplate automated. if you will do a thing more than twice, it is a script. everything past these five is decorative. know these cold. and the habit that ties it together: ask the AI itself. for the config, for the error, for any of it, let the agent do the lifting, then double check what it hands you. lock the five, build the habit, and you make it. skip it, anon, and you ngmi.
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LMSYS Org
LMSYS Org@lmsysorg·
🐋 DeepSeek V4 is now merged into SGLang main with v0.5.12. What we shipped at launch: 🔹 ShadowRadix: native prefix caching for V4's hybrid attention 🔹 HiSparse: CPU-extended KV for sparse attention (up to 3× long-context throughput) 🔹 MTP speculative decoding with in-graph metadata preparation 🔹 W4A8 MegaMoE kernel 🔹 Flash Compressor + Lightning TopK kernels 🔹 Multiple parallelism methods: Tensor Parallelism/Expert Parallelism/Context Parallelism/Data Parallelism Attention 🔹 Prefill Decode Disaggregation 🔹 Hardware: H100, H200, B200, B300, GB200, GB300, MI35X And what we added since: 🔹 HiCache for V4 under UnifiedRadixTree 🔹 W4A4 MegaMoE kernels for faster MegaMoE 🔹 Marlin/FlashInfer MXFP4 (W4A16) MoE on Hopper 🔹 Hierarchical multi-stream overlap for small-batch decode 🔹 Optimized mHC pipeline: DeepGemm + fused norm + fused hc_head 🔹 Faster KV Compression V2 kernel 🔹 Fused SiLU+clamp+FP8 quantization kernel 🔹 Support TP16 on H100/H20 🔹 Support Multiple Detokenizers 🔹Pipeline Parallelism 🔹One docker image for all supported Nvidia hardware Thanks to @NVIDIAAI, @AMD, @ant_oss, @alibaba_cloud, ByteDance, @iFLYTEKLab, @radixark, and @pranjalssh for the work we shipped together on V4 🙌 More in 0.5.12 👇
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ogx
ogx@ogx5125·
@fchollet 代码量10x,但系统复杂度往往是指数级增长,调试和维护的时间把新增价值都吃掉了
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François Chollet
François Chollet@fchollet·
The quantity of code that devs ship has roughly 10xed. But net developer productivity (value created by unit of time) is only up by a bit, if at all. Part of it is that the additional code is solving more incremental problems. A bigger part is that the new code is creating problems of its own.
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Btc老陈
Btc老陈@btclaochen·
对于高敏感人群 物哀之美是无理由的解药 短暂即永恒 惊鸿一瞥 完成自我救赎 只想停在那里,不想离开 #高敏感人群如何治愈 #物哀美学
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Tom Huang
Tom Huang@tuturetom·
Open Design 正式突破 40k Star🚀 这是开源项目的一大步,在 Github 历史上可能不超过 1000 个,这是全球开放开源的胜利🔥 我们也同步发布 0.8.0 preview 版本,将 Open Design 带入 Everywhere 的时代,支持插件系统,内置超过 400+插件,可以自行开发插件并接入 Design Agent Engine 欢迎尝试
Open Design@nexudotio

🎉40K stars in 16 days — Open Design is now the #2 fastest-growing repo of all time on GitHub among 40K+ projects. In those 16 days we shipped: → 8 releases · 622 commits · 175 external contributors → 113 Skills · 149 Design Systems · 110 Templates → 16 coding-agent CLIs auto-detected → Live Artifacts · HTML-in-Canvas · Auto-memory → Cloudflare Pages deploy · MCP server + client → 13 UI languages Huge thanks to all of you 👏 ↓ github.com/nexu-io/open-d…

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ogx
ogx@ogx5125·
@steipete sqlc 用最熟悉的 SQL 写查询,却获得比 ORM 更安全、更快的 Go 体验
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ogx
ogx@ogx5125·
@suchenzang 需要更好的自我调节机制,否则热情会变成消耗品
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Susan Zhang
Susan Zhang@suchenzang·
if you're in tech for the love of technology and not just for the money, and somehow don't experience some form of burnout/existential crises every other year, you're probably doing it wrong
Deedy@deedydas

The vibes in SF feel pretty frenetic right now. The divide in outcomes is the worst I've ever seen. Over the last 5yrs, a group of ~10k people - employees at Anthropic, OpenAI, xAI, Nvidia, Meta TBD, founders - have hit retirement wealth of well above $20M (back of the envelope AI estimation). Everyone outside that group feels like they can work their well-paying (but <$500k) job for their whole life and never get there. Worse yet, layoffs are in full swing. Many software engineers feel like their life's skill is no longer useful. The day to day role of most jobs has changed overnight with AI. As a result, 1. The corporate ladder looks like the wrong building to climb. Everyone's trying to align with a new set of career "paths": should I be a founder? Is it too late to join Anthropic / OpenAI? should I get into AI? what company stock will 10x next? People are demanding higher salaries and switching jobs more and more. 2. There’s a deep malaise about work (and its future). Why even work at all for “peanuts”? Will my job even exist in a few years? Many feel helpless. You hear the “permanent underclass” conversation a lot, esp from young people. It's hard to focus on doing good work when you think "man, if I joined Anthropic 2yrs ago, I could retire" 3. The mid to late middle managers feel paralyzed. Many have families and don't feel like they have the energy or network to just "start a company". They don't particularly have any AI skills. They see the writing on the wall: middle management is being hollowed out in many companies. 4. The rich aren’t particularly happy either. No one is shedding tears for them (and rightfully so). But those who have "made it" experience a profound lack of purpose too. Some have gone from <$150k to >$50M in a few years with no ramp. It flips your life plans upside down. For some, comparison is the thief of joy. For some, they escape to NYC to "live life". For others still, they start companies "just cuz", often to win status points. They never imagined that by age 30, they'd be set. I once asked a post-economic founder friend why they didn't just sell the co and they said "and do what? right now, everyone wants to talk to me. if i sell, I will only have money." I understand that many reading this scoff at the champagne problems of the valley. Society is warped in this tech bubble. What is often well-off anywhere else in the world is bang average here. Unlike many other places, tenure, intelligence and hard work can be loosely correlated with outcomes in the Bay. Living through a societally transformative gold rush in that environment can be paralyzing. "Am I in the right place? Should I move? Is there time still left? Am I gonna make it?" It psychologically torments many who have moved here in search of "success". Ironically, a frequent side effect of this torment is to spin up the very products making everyone rich in hopes that you too can vibecode your path to economic enlightenment.

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vLLM
vLLM@vllm_project·
vLLM v0.21.0 is out! 367 commits from 202 contributors (49 new). 🎉 Highlights: KV Offload + HMA, spec decode with thinking budget (reasoning models), TOKENSPEED_MLA on Blackwell for DSR1 / Kimi K2.5, Mooncake distributed KV, DeepSeek V4 pipeline parallelism. C++20 + Transformers v5 baseline. Thread 👇
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ogx
ogx@ogx5125·
@thsottiaux 感谢快速定位并修复,很专业
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Tibo
Tibo@thsottiaux·
We found and fixed two issues that could explain this degradation of the capability of GPT-5.5 in Codex over the last ~ 48 hours. We are monitoring over the coming hours to fully confirm and I will reset usage limits this evening. Apologies and now is the time for /fast maxxing.
Tibo@thsottiaux

Codex team is aware of reports of GPT-5.5 performing worse for some users and investigating. We don't have anything conclusive yet and systems are healthy but we will share updates as we go.

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ogx
ogx@ogx5125·
@levie 软件是交付即结束,AI是交付即开始
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Aaron Levie
Aaron Levie@levie·
I’m fully forward deployed engineering pilled specifically because AI simply is not the same as software. In software, you deliver a stable piece of technology to a customer and they adopt it and that’s that (extreme over simplification). In AI, you’re delivering something that is constantly evolving both due to the nature of the new capabilities and best practices that emerge, but also because the underlying models change so much that they can meaningfully change the workflow as a result of their upgrades. For this reason it’s far more logical that one vendor can share best practices across thousands of companies more efficiently than every single company can learn and manage these best practices themselves. Further, the learnings from those customers should go right back into the core product as a result. As we go from chat systems to anyone can relatively easily adopt to agentic systems that require more meaningful efforts to manage and update, the FDE model (or equivalent) essentially becomes a core competency for anyone deploying AI at scale.
Yash Patil@ypatil125

The real power of forward deployed engineering has always been putting strong technical people directly alongside the operators who own the outcome. That proximity forces the work to solve the actual problem instead of some sanitized version of it. In the AI era this principle has become even more valuable. Agents can now sit inside real workflows and improve from actual decisions, which means the highest-leverage work is extracting the tacit knowledge that lives with subject matter experts, building evaluations that reflect how things actually break, and closing the production feedback loop so agents get better from real outcomes.

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