Matt Makai | Full Stack Python | Plushcap

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Matt Makai | Full Stack Python | Plushcap

Matt Makai | Full Stack Python | Plushcap

@fullstackpython

Software dev. Expertise in DevRel & DX. Currently building https://t.co/qpRK3z9jV0. Prev DevRel leader @twilio & VP DevRel @digitalocean @AssemblyAI @launchdarkly

Washington, DC Katılım Ocak 2015
317 Takip Edilen90.4K Takipçiler
Matt Makai | Full Stack Python | Plushcap retweetledi
antirez
antirez@antirez·
This is now pushed on the repository. The 75 tests are 25 each for GPQA Diamond / Super GPQA / AIME2025. They are roughly ordered by complexity. In the picture: 4 bit. 2 bit scores a bit worse, not dramatically \o/. This test is not designed to be all-passed, it puts the model at play with hard Q. Useful to improve GGUF files and catch inference errors ASAP.
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José Valim
José Valim@josevalim·
The whole Anthropic kerfuffle would have gone much smoother if they had been upfront about it. "Hey, we know this is unpopular, but we are moving programmatic access to API pricing. To easen the transition, we are giving API credits that match your subscription value. We also expect this change to increase capacity, so we are doubling the limits throughout Claude products for the next 2 months". The reason they made it sound like an upgrade was because the announcement was not for developers. It was for investors and enterprise customers. Impacting devrel is just collateral damage, which is on par for a company which believes coding is going away any time now. And this is extremely disapointing because they want to position themselves as a company that we should trust. But if they can't be honest about pricing changes, it is really hard to believe them on anything else.
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Gergely Orosz
Gergely Orosz@GergelyOrosz·
There are few things as lame as dunking on an engineer asking for feedback, esp in public. Honestly, a dev that has asked for feedback *once* in their lives, *in public* is already top 1% or above. Doing it regularly is somewhere too 0.0X% It adds up+makes big differences
Dwayne@CtrlAltDwayne

Poor Boris at Anthropic is trying his hardest, but the fact he has to ask how they can do better goes to show Anthropic has literally no clue why people are ditching Claude Code for Codex.

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clem 🤗
clem 🤗@ClementDelangue·
Local open-weight AI on a laptop has been improving more than twice as fast as Moore's Law! Between May 2024 and May 2026, the most expensive MacBook Pro you could buy stayed at 128 GB of unified memory. The hardware ceiling barely moved. But the smartest open-weight model from @huggingface you could actually run on it went from a score of 10 (Llama 3 70B) to 47 (DeepSeek V4 Flash on @antirez's mixed-Q2 GGUF) on the @ArtificialAnlys Intelligence Index. That is 4.7× in 24 months, or a doubling of intelligence every 10.7 months. Moore's Law (transistor count) doubles every 24 months. Local open-weight AI on a laptop has been improving more than twice as fast as Moore's Law, on completely unchanged hardware.
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Matt Makai | Full Stack Python | Plushcap
@antirez Re-downloaded from HF 30 mins ago, it’s working for me on Macbook M3 Max 128gb. Tried both w/MTP and w/o MTP while using Pi on some projects. Didn’t see any speed up from MTP (first shot is new q2 without MTP, second is with MTP), but no issues. Amazing project, keep it going!
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antirez
antirez@antirez·
I uploaded the imatrix 2 bit quants on my Hugging Face. They should be better than the old version, but before updating the download script, if you could help me with some testing reporting the feelings here, I would appreciate :)
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antirez
antirez@antirez·
DS4 running on DGX Spark (GB10 / CUDA), private branch for now. 12 tokens/sec, the memory bandwidth is limited in this system, at 270GB/sec. But prefill is ways more alighed to M3 Max at ~200 t/s. I'll release when more mature, but it is almost sure that it will get merged.
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Ali Khayrallah
Ali Khayrallah@alisk·
@CWood_sdf @fullstackpython You’re both half right. It was called a programming language for a reason. If a spec looks like pseudo code which looks like code…
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Matt Makai | Full Stack Python | Plushcap retweetledi
Chris Wood
Chris Wood@CWood_sdf·
i love how people are saying "if we write a sufficiently detailed specification, the agent can write all our code" do you know what writing a sufficiently detailed specification that deterministically maps to what a computer's actions is? it's coding
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Matt Makai | Full Stack Python | Plushcap
Just added @OpenObserve to Plushcap (link: plushcap.com/openobserve). The company was founded in 2022 with a $10m Series A raised last month. They ramped technical content hard before that with 69 posts published in 2026 (256 blog posts all time). That output velocity is a typical pattern for a DevTools PLG company to make the seed to Series A jump. Their content strategy involves high output SEO/GEO and integration tutorials. They've published a 9-part "Datadog vs OpenObserve" series covering logs, metrics, traces, dashboards, alerts, RUM, pipelines, IAM, and cost. On top of that, dedicated "Top 10 alternatives" posts targeting Datadog, Grafana, Splunk, New Relic, and more. They're publishing integration tutorials across AWS, Azure, GCP, plus databases like PostgreSQL, MongoDB, and ClickHouse. More recently they pivoted into AI/LLM observability content including how to monitor OpenAI API costs, trace LangChain and LlamaIndex apps, observe MCP servers, and monitor production AI agents. YouTube is still early (960 subscribers, 91K views across 52 videos) but output matches the pace of their written output. Full data for blog output, YouTube metrics, competitive positioning, and website breakdown: lnkd.in/dVJUfZw5
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Tuomas Artman
Tuomas Artman@artman·
Today is a hard day. I shared this note with the @linear team today: We’ve made the difficult decision to increase our workforce. This is not a cost-cutting exercise or a reflection of anyone’s performance. We’re simply reimagining every role for the agentic AI era. We’re hiring. We’re sorry about that.
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Matt Makai | Full Stack Python | Plushcap
Coinbase looking to continue in the long tradition of crypto exchange death by hacking 🙏
Brian Armstrong@brian_armstrong

This is an email I sent earlier today to all employees at Coinbase: Team, Today I’ve made the difficult decision to reduce the size of Coinbase by ~14%. I want to walk you through why we're doing this now, what it means for those affected, and how this positions us for the future. Why now Two forces are converging at the same time. We need to be front footed to respond to both. First, the market. Coinbase is well-capitalized, has diversified revenue streams, and is well-positioned to weather any storm. Crypto is also on the verge of the next wave of adoption, with stablecoins, prediction markets, tokenization, and more taking off. However, our business is still volatile from quarter to quarter. While we've managed through that cyclicality many times before and come out stronger on the other side, we’re currently in a down market and need to adjust our cost structure now so that we emerge from this period leaner, faster, and more efficient for our next phase of growth. Second, AI is changing how we work. Over the past year, I’ve watched engineers use AI to ship in days what used to take a team weeks. Non-technical teams are now shipping production code and many of our workflows are being automated. The pace of what's possible with a small, focused team has changed dramatically, and it's accelerating every day. All of this has led us to an inflection point, not just for Coinbase, but for every company. The biggest risk now is not taking action. We are adjusting early and deliberately to rebuild Coinbase to be lean, fast, and AI-native. We need to return to the speed and focus of our startup founding, with AI at our core. What this means To get there, we are not just reducing headcount and cutting costs, we’re fundamentally changing how we operate: rebuilding Coinbase as an intelligence, with humans around the edge aligning it. What does this mean in practice? - Fewer layers, faster decisions: We are flattening our org structure to 5 layers max below CEO/COO. Layers slow things down and create coordination tax. The future is small, high context teams that can move quickly. Leaders will own much more, with as many as 15+ direct reports. Fewer layers also means a leaner cost structure that is built to perform through all market cycles. - No pure managers: Every leader at Coinbase must also be a strong and active individual contributor. Managers should be like player-coaches, getting their hands dirty alongside their teams. - AI-native pods: We’ll be concentrating around AI-native talent who can manage fleets of agents to drive outsized impact. We’ll also be experimenting with reduced pod sizes, including “one person teams” with engineers, designers, and product managers all in one role. In short: AI is bringing a profound shift in how companies operate, and we’re reshaping Coinbase to lead in this new era. This is a new way of working, and we need to leverage AI across every facet of our jobs. To those who are affected I know there are real people behind these decisions — talented colleagues who have poured themselves into this company and our mission. To those of you who will be leaving: thank you. You’ve helped build Coinbase into what it is today, and I am sincerely grateful for everything you've done. All impacted team members will receive an email to their personal account in the next hour with more information, and an invitation to meet with an HRBP and a senior leader in your organization. Coinbase system access has been removed today. I know this feels sudden and harsh, but it is the only responsible choice given our duty to protect customer information. To those affected, we will be providing a comprehensive package to support you through this transition. US employees will receive a minimum of 16 weeks base pay (plus 2 weeks per year worked), their next equity vest, and 6 months of COBRA. Employees on a work visa will get extra transition support. Those outside of the US will receive similar support, based on local factors and subject to any consultation requirements. Coinbase prides itself on talent density. Our employees are among the most talented people in the world, and I have no doubt that your skills and experience will be highly sought after as you pursue your next chapters. How we move forward To the team that is staying, I know this is a difficult day. We’re saying goodbye to colleagues and friends you've been in the trenches with. But here’s what I want you to know as we move forward together: Over the past 13 years, we have weathered four crypto winters, gone public, and built the most trusted platform in our industry. We’ve made it this far by making hard decisions and by always staying focused on our mission. This time will be no different – nothing has changed about the long term outlook of our company or industry. And most importantly, our mission has never been more important for the world. Increasing economic freedom requires a new financial system, and we’re building it. The Coinbase that emerges from this will be more capable than ever to achieve our mission. Brian

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Catherine Yeo
Catherine Yeo@catherinehyeo·
Love seeing Naomi Osaka honor the CLRS Algorithms textbook at this year's Met Gala
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Matt Makai | Full Stack Python | Plushcap
Exceptional episode well worth a listen/watch
Gergely Orosz@GergelyOrosz

OpenClaw - the agentic software spreading like wildfire - was built on top of Pi, a minimalist, self-modifying agent. I sat down with Pi's creator, @badlogicgames and longtime Pi user (+ the creator of Flask) @mitsuhiko to talk Pi, and their (very grounded!) takes on building with AI. Timestamps: 00:00 Intro 07:30 How Mario, Armin, and Peter Steinberger met 15:15 How 30 dev teams use AI agents: learnings 21:50 The importance of judgment 24:26 Challenges when non-engineers write code 28:30 Downsides of over-automation 32:18 Pi 48:09 OpenClaw + Pi 50:54 “Clankers” 57:32 Open source and AI 1:00:22 Complexity as the enemy 1:02:50 Building an AI-native startup 1:11:52 “Slow the F down” 1:16:40 MCPs vs. CLI 1:25:03 Predictions and staying up to date • YouTube: youtu.be/n5f51gtuGHE • Spotify: open.spotify.com/episode/1fDw9c… • Apple: podcasts.apple.com/us/podcast/bui… Brought to you by: • @statsig  – ⁠ The unified platform for flags, analytics, experiments, and more. statsig.com/pragmatic • @SonarSource  — The makers of SonarQube, the industry standard for code verification and automated code review. Try it out for yourself. sonarsource.com/plans-and-pric… • @WorkOS  – WorkOS gives you APIs to ship enterprise features – SSO, directory sync, RBAC, audit logs – in days, not months. Visit WorkOS.com to learn more. --- Three parts I found especially interesting in this discussion: 1. New trend: AI makes it harder for senior engineers to reject pointless complexity. Historically, senior engineers kept software complexity at bay simply by saying “no” a lot. But Armin observes that these days, more junior engineers and product managers deploy agent-scripted counterarguments when a senior colleague kicks an idea to the curb. This makes decision-making exhausting, and more bad ideas make it into production as a result. 2. It should be MUCH easier to build specialized tools for specific tasks. Different projects need different harness types because, as Mario points out, the same hammer is not ideal for every single construction job. As such, Pi is built with the goal of allowing the creation of specialized harnesses. It can modify itself so that a user can create the bespoke harness needed for any task. Mario believes it’s a preview of how self-modifiable software might look in the future. 3. Automation bias is one of the biggest risks of working with AI agents. Once devs confirm that an AI agent can produce acceptable code, they start to review its output less often, even though agents can – and do! – produce slop. Mario advises being far more sceptical with agents, and cautions that the quality of their output isn’t guaranteed, however well they performed previously.

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stochasm
stochasm@stochasticchasm·
hey can you launch 4 sub-goblins to investigate each of these issues
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Patrick Loeber
Patrick Loeber@patloeber·
Lately I've been having fun with running coding agents fully locally. The setup I landed on is: - Pi agent - Gemma 4 26B A4B - Server of choice: LM Studio/Ollama/llama.cpp I wrote a step-by-step guide with instructions on how to set it up: patloeber.com/gemma-4-pi-age…
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Gergely Orosz
Gergely Orosz@GergelyOrosz·
The only people who believe any of this are non-coders. I tried to build a game (an area I’m an n00b in.) The results are amusingly disastrous - I never before coded a decent game. But I’ll crack out backend services w AI rapidly - because I coded dozens of them before…
AI Edge@aiedge_

Anthropic CEO (Dario Amodei): "Coding is going away first, then all of software engineering." What do you think about this?

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Matt Makai | Full Stack Python | Plushcap retweetledi
Macrophysiological System🐀
Macrophysiological System🐀@InVitroFuture·
I find LLMs very helpful for scientific writing. I do the legwork of planning out what to say, track down citations, sketch out the flow, feed all of this to the LLM to generate a draft, and the output is so awful it motivates me to write it out the right way in disgust
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