github.blog/changelog/2026-0… they fixed it.

It's finally happening. We can put this whole install script NPM worm madness behind us... github.blog/changelog/2026-0…

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🔨agent experience is the new developer experience 🗺️agent trajectories are the new friction logs 🧨The path to 10xing does not come from increasing token burn ... Efficacy of task and laser focus on understanding what is going wrong is the bottleneck - NOT YOUR MODEL.

Everyone is waking up to the fact that code-review is the bottleneck of 2026 I'm bullish on ramping review agents - but the vibes coming from - 0github.com - asi.review - pierre.co start to hit closer to home on where we need to go

Openclaw to GitHub this month.

“AI will replace you” Me who already replaced myself with Claude:

For earth day, Pierre Computer Company is excited to announce the first public preview of… TREES[DOT]SOFTWARE A new, feature rich trees library – tuned for machines. Demo of AOSP fixture (1.5 MILLION files with fixed dirs - no blanking)

This is cool: maggieappleton.com/zero-alig…

Yes, you can technically run an LLM on a 1998 iMac G3 with 32 MB of RAM. Prompt: "The green goblin" Output: "The green goblin had a big mop. She had a cow in the field too. I" Hardware: • Stock iMac G3 Rev B (October 1998). 233 MHz PowerPC 750, 32 MB RAM, Mac OS 8.5. No upgrades. • Model: @karpathy's 260K TinyStories (Llama 2 architecture). ~1 MB checkpoint. Toolchain: • Cross-compiled from a Mac mini using Retro68 (GCC for classic Mac OS → PEF binaries) • Endian-swapped model tokenizer from little-endian to big-endian for PowerPC • Files transferred via FTP to the iMac over Ethernet Challenges: • Mac OS 8.5 gives apps a tiny memory partition by default. Had to use MaxApplZone() NewPtr() from the Mac Memory Manager to get enough heap • RetroConsole crashes on this hardware, so all output writes to a text file you open in SimpleText • The original llama2.c weight layout assumes n_kv_heads == n_heads. The 260K model uses grouped-query attention (kv_heads=4, heads=8), which shifted every pointer after wk and produced NaN. Fixed by using n_kv_heads * head_size for wk/wv sizing • Static buffers for the KV cache and run state to avoid malloc failures on 32 MB It reads a prompt from prompt.txt, tokenizes with BPE, runs inference, and writes the continuation to output.txt. Fun!

#1 mistake I see people making when prompt coding is omitting verification instructions. The delta in outcomes btw a prompt that explicitly lists a verification step and not is significant. Often it's as simple as "Optimize code in main . py" vs "Profile and optimize"

Ran out of tokens Mac minis on backorder Back to analog

The cost of code nearing 0 is great and all - but i'm more excited about the burden of tech-debt dropping to zero.

𝕿𝖍𝖎𝖗𝖙𝖊𝖊𝖓𝖙𝖍 𝚘𝚙𝚎𝚗𝚌𝚘𝚍𝚎 PR 💀

If you know you KNOW

Openclaw is a glimpse into the future I expect to materialize within the next 3 months for everyone who today is only using the web front ends of the frontier models. The oh s*** moment that is happening right now is a result of lowering the barrier to entry for the highly technical early adopters - demonstrating just how much is possible today if you string all of the pieces together with the existing models