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A hot potato: A fed­eral case in Atlanta is rais­ing ques­tions about a pri­vacy-fo­cused mo­bile op­er­at­ing sys­tem, with pros­e­cu­tors ar­gu­ing that its fea­tures were used to erase ev­i­dence. The US Department of Justice is at­tempt­ing to pros­e­cute Atlanta res­i­dent Sam Tunick un­der a fed­eral statute that makes it a crime to de­stroy prop­erty in an ef­fort to pre­vent it from be­ing seized.

The case cen­ters on Tunick’s use of GrapheneOS, an open-source op­er­at­ing sys­tem that works on Google Pixel phones and lets users en­ter a pass­code to wipe a de­vice clean.

Experts said the le­gal ap­proach is un­usual and may be the first time the law has been aimed at an op­er­at­ing sys­tem. “It’s con­cern­ing — and sends the mes­sage that [GrapheneOS] is crim­i­nal by de­fault,” said Christophe Boutry, a cy­ber­se­cu­rity and sur­veil­lance ex­pert. Boutry and Bill Buddington, se­nior staff tech­nol­o­gist at the Electronic Frontier Foundation, both said they had not seen a sim­i­lar case.

The in­ci­dent be­gan at Hartsfield-Jackson Atlanta International Airport on January 24 of last year. Tunick had just re­turned from a trip to the Dominican Republic when he was stopped for ques­tion­ing. According to court tes­ti­mony, fed­eral agents had al­ready cir­cu­lated his name and photo in­ter­nally, say­ing he was un­der in­ves­ti­ga­tion for “suspected ter­ror­ism ac­tiv­i­ties” be­cause of his al­leged as­so­ci­a­tion with the move­ment against Cop City.

Tunick was taken to a sec­ondary screen­ing room, where mul­ti­ple agents ques­tioned him. A mo­tion filed by his de­fense ar­gues the in­ter­ro­ga­tion fo­cused on child sex­ual abuse ma­te­r­ial as a pre­text for in­ves­ti­gat­ing his con­nec­tions to the protest move­ment. The mo­tion also states that Tunick asked four times to speak with a lawyer and was de­nied each time. According to the same fil­ing, agents did not pre­sent a war­rant or read him his rights.

Government at­tor­neys and agents pushed back dur­ing Monday’s hear­ing. They de­scribed the en­counter as a rou­tine air­port in­spec­tion. Larry Findley, a Customs and Border Protection of­fi­cer, said agents were “looking for any­thing that’s pro­hib­ited.”

During the ques­tion­ing, agents re­peat­edly asked Tunick to un­lock his phone and warned they would seize it if he re­fused. When he fi­nally pro­vided a pass­code, the phone ap­peared to restart. The de­fense mo­tion states that “the screen went blank, flashed sev­eral times, and the phone ap­peared to restart,” re­sult­ing in the loss of data.

The wipe is now cen­tral to the case. Prosecutors are treat­ing it as an in­ten­tional act to de­stroy ev­i­dence, while the de­fense ar­gues that the search vi­o­lated Tunick’s con­sti­tu­tional rights and that the ev­i­dence should be sup­pressed.

The case raises ques­tions about which con­sti­tu­tional rights ap­ply at US bor­ders, in­clud­ing in­ter­na­tional air­ports, where au­thor­i­ties have broader search pow­ers. A judge is not ex­pected to rule on the de­fense mo­tion un­til at least late October.

GrapheneOS is de­signed to im­prove pri­vacy and se­cu­rity on Pixel phones. Supporters say those tools are le­git­i­mate se­cu­rity pro­tec­tions, not ev­i­dence of crim­i­nal in­tent. Boutry pointed to France and Spain, where au­thor­i­ties have strug­gled to gain ac­cess to se­cured de­vices. He said au­thor­i­ties have treated the use of GrapheneOS it­self as sus­pi­cious. In Catalonia, Spain, po­lice have been pro­fil­ing peo­ple car­ry­ing Pixel phones, as­sum­ing they have GrapheneOS in­stalled and are drug deal­ers or gang mem­bers.

“The main goal [of the op­er­at­ing sys­tem] is pro­tec­tion of pri­vacy,” Boutry said. “They’re our phones and the state can’t tell us how to use them.”

The case is tied to on­go­ing op­po­si­tion to Cop City, a $109 mil­lion po­lice train­ing fa­cil­ity that opened last spring. The pro­ject has drawn op­po­si­tion from ac­tivists con­cerned about po­lice mil­i­ta­riza­tion and en­vi­ron­men­tal im­pacts. Law en­force­ment of­fi­cials have de­fended it as nec­es­sary for train­ing and re­cruit­ment.

Previous at­tempts to pros­e­cute pro­test­ers at the state level have foundered, while fed­eral au­thor­i­ties have more re­cently stepped in, in­clud­ing a sep­a­rate in­dict­ment an­nounced last month.

📰  Tech Blog |     📄  Full Report

1. Model Introduction

Kimi K3 is an open-weight, na­tive mul­ti­modal agen­tic model and our most ca­pa­ble model to date. It is a 2.8T-parameter model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), with na­tive vi­sion ca­pa­bil­i­ties and a 1-million-token con­text win­dow. It is the world’s first open 3T-class model, de­signed for fron­tier in­tel­li­gence across long-hori­zon cod­ing, knowl­edge work, and rea­son­ing.

Key Features

New Architecture: Kimi K3 is built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), and scales up MoE spar­sity with a Stable LatentMoE frame­work that ac­ti­vates 16 out of 896 ex­perts — yield­ing an ap­prox­i­mate 2.5× im­prove­ment in over­all scal­ing ef­fi­ciency over Kimi K2.

Long-Horizon Coding: Operating with min­i­mal hu­man over­sight, Kimi K3 sus­tains long en­gi­neer­ing ses­sions, nav­i­gates mas­sive repos­i­to­ries, and or­ches­trates ter­mi­nal tools — from GPU ker­nel op­ti­miza­tion and com­piler de­vel­op­ment to vi­sion-in-the-loop game dev, CAD, and even chip de­sign.

Agentic Knowledge Work: Kimi K3 ad­vances end-to-end knowl­edge work, pro­duc­ing deep re­search with in­ter­ac­tive vi­su­al­iza­tions, wid­gets and dash­boards, and mo­tion de­sign and video edit­ing, pow­ered by its na­tive mul­ti­modal ar­chi­tec­ture.

Native Multimodality & Long Context: Kimi K3 un­der­stands text, im­ages, and video within the same model, and sup­ports a 1-million-token con­text win­dow.

Open Frontier Weights: We re­lease the full Kimi K3 model weights un­der the Kimi K3 License, mak­ing fron­tier in­tel­li­gence openly avail­able for re­search, de­ploy­ment, and fur­ther in­no­va­tion.

2. Model Summary

3. Evaluation Results

All Kimi K3 re­sults are ob­tained with rea­son­ing ef­fort set to ‘max’ and tem­per­a­ture = 1.0. For sin­gle-step tasks, such as GPQA Diamond, HLE-Full, and vi­sion bench­marks with­out tools, we set top-p = 0.95; for agen­tic tasks, we set top-p = 1.0. For HLE-Full, MMMU-Pro, CharXiv (RQ), MathVision, and ZeroBench, each cell re­ports the scores with­out and with tool aug­men­ta­tion (general tools for HLE-Full, Python for the vi­sion bench­marks), in that or­der.

Reasoning & knowl­edge bench­marks CritPt and AA-LCR. Scores are cited from Artificial Analysis as of July 23, 2026.

CritPt and AA-LCR. Scores are cited from Artificial Analysis as of July 23, 2026.

Coding bench­marks DeepSWE. Kimi K3 is eval­u­ated with the Kimi Code har­ness. The GLM-5.2 score is taken from the GLM-5.2 re­lease blog; all re­main­ing scores are from the of­fi­cial DeepSWE leader­board, un­der which Kimi K3 at­tains 67.3 with the mini-SWE-agent har­ness. We re­port the DeepSWE v1.1 tasks. Terminal-Bench 2.1. Kimi K3 is eval­u­ated with the Kimi Code har­ness. For all other mod­els, we re­port the best score across har­nesses: GLM-5.2 with Claude Code (GLM-5.2 re­lease blog); Claude Opus 4.8 and Claude Fable 5 with Terminus 2 (Artificial Analysis); GPT-5.5 and GPT-5.6 Sol with Codex (OpenAI). ProgramBench. Kimi K3 is eval­u­ated with the Kimi Code har­ness. The GLM-5.2 score is from the GLM-5.2 re­lease blog; all other scores are from Vals AI. SWE-Marathon. Kimi K3, Claude Opus 4.8, and Claude Fable 5 are eval­u­ated with the Claude Code har­ness; GPT-5.6 Sol is eval­u­ated with the Codex har­ness. The GLM-5.2 score is from the GLM-5.2 re­lease blog. Our eval­u­a­tion is based on an H20-calibrated branch of the of­fi­cial tasks as of July 9, 2026, prior to the fi­nal v1.1 re­lease: the Docker im­ages, per­for­mance gates, and ref­er­ence or­a­cles for the GPU tasks have been re­cal­i­brated for H20, while the cor­rect­ness and anti-cheat val­ida­tors re­main un­changed. Additionally, Claude Fable 5 hit fall­backs on 35% of the tasks in our eval­u­a­tion, which may have neg­a­tively im­pacted its mea­sured per­for­mance. FrontierSWE. Kimi K3 is eval­u­ated with the Kimi Code har­ness and GPT-5.6 Sol with the Codex har­ness; all other re­sults are from FrontierSWE. Dominance scores are re­com­puted from the raw scores us­ing the of­fi­cial eval­u­a­tion script and are cur­rent as of July 16, 2026. PostTrainBench. Scores for GLM-5.2, GPT-5.5, and Claude Opus 4.8 are adopted from the of­fi­cial PostTrainBench re­sults. Kimi K3, Claude Fable 5, and GPT-5.6 Sol are eval­u­ated with the of­fi­cial Harbor im­ple­men­ta­tion at max­i­mum rea­son­ing ef­fort, av­er­aged over three runs on H20 GPUs (instead of H100 in the of­fi­cial set­ting) — Kimi K3 and Claude Fable 5 with the Claude Code har­ness, and GPT-5.6 Sol with the Codex har­ness. MLS-Bench-Lite. Kimi K3 is eval­u­ated with the Kimi Code har­ness; GLM-5.2 and the Claude mod­els with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol with the Codex har­ness. SciCode. Scores are cited from Artificial Analysis as of July 23, 2026. Kimi Code Bench 2.0 (in-house). Kimi K3 is eval­u­ated with the Kimi Code har­ness (it at­tains 73.7 with the Claude Code har­ness); GLM-5.2, Claude Opus 4.8, and Claude Fable 5 with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol with the Codex har­ness. All mod­els are eval­u­ated at max­i­mum rea­son­ing ef­fort, ex­cept GPT-5.5, which uses the “xhigh” set­ting. As the bench­mark in­cludes cy­ber­se­cu­rity and safety-re­lated tasks, we also dis­close the frac­tion of re­fused or fall­back tasks: Claude Fable 5 hit 13 fall­backs and 1 re­fusal out of 80 tasks; 10 re­fusals out of 80 tasks en­tered GPT-5.6 Sol’s cy­ber guard; GPT-5.5 had 3 re­fusals out of 80 tasks.

DeepSWE. Kimi K3 is eval­u­ated with the Kimi Code har­ness. The GLM-5.2 score is taken from the GLM-5.2 re­lease blog; all re­main­ing scores are from the of­fi­cial DeepSWE leader­board, un­der which Kimi K3 at­tains 67.3 with the mini-SWE-agent har­ness. We re­port the DeepSWE v1.1 tasks.

Terminal-Bench 2.1. Kimi K3 is eval­u­ated with the Kimi Code har­ness. For all other mod­els, we re­port the best score across har­nesses: GLM-5.2 with Claude Code (GLM-5.2 re­lease blog); Claude Opus 4.8 and Claude Fable 5 with Terminus 2 (Artificial Analysis); GPT-5.5 and GPT-5.6 Sol with Codex (OpenAI).

ProgramBench. Kimi K3 is eval­u­ated with the Kimi Code har­ness. The GLM-5.2 score is from the GLM-5.2 re­lease blog; all other scores are from Vals AI.

SWE-Marathon. Kimi K3, Claude Opus 4.8, and Claude Fable 5 are eval­u­ated with the Claude Code har­ness; GPT-5.6 Sol is eval­u­ated with the Codex har­ness. The GLM-5.2 score is from the GLM-5.2 re­lease blog. Our eval­u­a­tion is based on an H20-calibrated branch of the of­fi­cial tasks as of July 9, 2026, prior to the fi­nal v1.1 re­lease: the Docker im­ages, per­for­mance gates, and ref­er­ence or­a­cles for the GPU tasks have been re­cal­i­brated for H20, while the cor­rect­ness and anti-cheat val­ida­tors re­main un­changed. Additionally, Claude Fable 5 hit fall­backs on 35% of the tasks in our eval­u­a­tion, which may have neg­a­tively im­pacted its mea­sured per­for­mance.

FrontierSWE. Kimi K3 is eval­u­ated with the Kimi Code har­ness and GPT-5.6 Sol with the Codex har­ness; all other re­sults are from FrontierSWE. Dominance scores are re­com­puted from the raw scores us­ing the of­fi­cial eval­u­a­tion script and are cur­rent as of July 16, 2026.

PostTrainBench. Scores for GLM-5.2, GPT-5.5, and Claude Opus 4.8 are adopted from the of­fi­cial PostTrainBench re­sults. Kimi K3, Claude Fable 5, and GPT-5.6 Sol are eval­u­ated with the of­fi­cial Harbor im­ple­men­ta­tion at max­i­mum rea­son­ing ef­fort, av­er­aged over three runs on H20 GPUs (instead of H100 in the of­fi­cial set­ting) — Kimi K3 and Claude Fable 5 with the Claude Code har­ness, and GPT-5.6 Sol with the Codex har­ness.

MLS-Bench-Lite. Kimi K3 is eval­u­ated with the Kimi Code har­ness; GLM-5.2 and the Claude mod­els with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol with the Codex har­ness.

SciCode. Scores are cited from Artificial Analysis as of July 23, 2026.

Kimi Code Bench 2.0 (in-house). Kimi K3 is eval­u­ated with the Kimi Code har­ness (it at­tains 73.7 with the Claude Code har­ness); GLM-5.2, Claude Opus 4.8, and Claude Fable 5 with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol with the Codex har­ness. All mod­els are eval­u­ated at max­i­mum rea­son­ing ef­fort, ex­cept GPT-5.5, which uses the “xhigh” set­ting. As the bench­mark in­cludes cy­ber­se­cu­rity and safety-re­lated tasks, we also dis­close the frac­tion of re­fused or fall­back tasks: Claude Fable 5 hit 13 fall­backs and 1 re­fusal out of 80 tasks; 10 re­fusals out of 80 tasks en­tered GPT-5.6 Sol’s cy­ber guard; GPT-5.5 had 3 re­fusals out of 80 tasks.

Agentic bench­marks OfficeQA Pro. Each test case pro­vides the agent with the en­tire PDF cor­pus, with all PDFs ren­dered as im­ages and no ma­chine-read­able text avail­able. OfficeQA Pro and SpreadsheetBench 2. Kimi K3, GLM-5.2, Claude Opus 4.8, and Claude Fable 5 are eval­u­ated with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol are eval­u­ated with the Codex har­ness. MCP-Atlas. All mod­els are eval­u­ated on the 500-task pub­lic sub­set with a 100-turn limit, us­ing Gemini 3.1 Pro as the judge. AutomationBench. All mod­els are eval­u­ated on the 600-task pub­lic sub­set, fol­low­ing the of­fi­cial GitHub setup in all other re­spects. BrowseComp. We adopt a con­text-com­paction strat­egy trig­gered at 300K to­kens. When eval­u­ated with the full 1M-token con­text win­dow and no con­text man­age­ment, Kimi K3 achieves a score of 90.4. The re­sults of Claude Fable 5, Claude Opus 4.8, GPT-5.6 Sol, and GPT-5.5 are cited from Anthropic and OpenAI. GDPval-AA v2, AA-Briefcase, τ³-Bank­ing, Harvey Lab-AA, and APEX-Agents. Scores are cited from Artificial Analysis and the APEX-Agents leader­board as of July 23, 2026. For Harvey Lab-AA, we re­port the cri­te­rion pass rate. CorpFin v2, Finance Agent v2, and Legal Research Bench. Scores are cited from Vals AI. Agents’ Last Exam. Scores are cited from the of­fi­cial leader­board as of July 23, 2026; we re­port the leader­board’s pri­mary pass-rate met­ric. On the leader­board, each model is paired with a spe­cific har­ness: Kimi K3 with Kimi Code; GPT-5.6 Sol and GPT-5.5 with Codex; Claude Fable 5, Claude Opus 4.8, and GLM-5.2 with Claude Code. † The Claude Fable 5 en­try runs at xhigh ef­fort with 40% of tasks an­no­tated as down­graded.

OfficeQA Pro. Each test case pro­vides the agent with the en­tire PDF cor­pus, with all PDFs ren­dered as im­ages and no ma­chine-read­able text avail­able.

OfficeQA Pro and SpreadsheetBench 2. Kimi K3, GLM-5.2, Claude Opus 4.8, and Claude Fable 5 are eval­u­ated with the Claude Code har­ness; GPT-5.5 and GPT-5.6 Sol are eval­u­ated with the Codex har­ness.

MCP-Atlas. All mod­els are eval­u­ated on the 500-task pub­lic sub­set with a 100-turn limit, us­ing Gemini 3.1 Pro as the judge.

AutomationBench. All mod­els are eval­u­ated on the 600-task pub­lic sub­set, fol­low­ing the of­fi­cial GitHub setup in all other re­spects.

BrowseComp. We adopt a con­text-com­paction strat­egy trig­gered at 300K to­kens. When eval­u­ated with the full 1M-token con­text win­dow and no con­text man­age­ment, Kimi K3 achieves a score of 90.4. The re­sults of Claude Fable 5, Claude Opus 4.8, GPT-5.6 Sol, and GPT-5.5 are cited from Anthropic and OpenAI.

GDPval-AA v2, AA-Briefcase, τ³-Bank­ing, Harvey Lab-AA, and APEX-Agents. Scores are cited from Artificial Analysis and the APEX-Agents leader­board as of July 23, 2026. For Harvey Lab-AA, we re­port the cri­te­rion pass rate.

CorpFin v2, Finance Agent v2, and Legal Research Bench. Scores are cited from Vals AI.

Agents’ Last Exam. Scores are cited from the of­fi­cial leader­board as of July 23, 2026; we re­port the leader­board’s pri­mary pass-rate met­ric. On the leader­board, each model is paired with a spe­cific har­ness: Kimi K3 with Kimi Code; GPT-5.6 Sol and GPT-5.5 with Codex; Claude Fable 5, Claude Opus 4.8, and GLM-5.2 with Claude Code. † The Claude Fable 5 en­try runs at xhigh ef­fort with 40% of tasks an­no­tated as down­graded.

Multimodal bench­marks Except for ZeroBench, which fol­lows the of­fi­cial set­ting and is run five times, all mul­ti­modal scores are av­er­aged over three runs. MMMU-Pro is eval­u­ated fol­low­ing the of­fi­cial pro­to­col, pre­serv­ing the orig­i­nal in­put or­der and prepend­ing im­ages to the text in­put. PerceptionBench is an in-house bench­mark that fo­cuses on atomic vi­sual per­cep­tion ca­pa­bil­i­ties.

Except for ZeroBench, which fol­lows the of­fi­cial set­ting and is run five times, all mul­ti­modal scores are av­er­aged over three runs. MMMU-Pro is eval­u­ated fol­low­ing the of­fi­cial pro­to­col, pre­serv­ing the orig­i­nal in­put or­der and prepend­ing im­ages to the text in­put.

PerceptionBench is an in-house bench­mark that fo­cuses on atomic vi­sual per­cep­tion ca­pa­bil­i­ties.

4. Native MXFP4 Quantization

Kimi K3 ap­plies quan­ti­za­tion-aware train­ing from the SFT stage on­ward, us­ing MXFP4 weights with MXFP8 ac­ti­va­tions for broad hard­ware com­pat­i­bil­ity.

5. Deployment

You can ac­cess Kimi K3′s API on https://​plat­form.kimi.ai by se­lect­ing kimi-k3, and we pro­vide OpenAI/Anthropic-compatible API for you. Currently, Kimi K3 is rec­om­mended to run on the fol­low­ing in­fer­ence en­gines:

You can ac­cess Kimi K3′s API on https://​plat­form.kimi.ai by se­lect­ing kimi-k3, and we pro­vide OpenAI/Anthropic-compatible API for you. Currently, Kimi K3 is rec­om­mended to run on the fol­low­ing in­fer­ence en­gines:

vLLM — see recipes

SGLang — see cook­book

TokenSpeed — see recipes

6. Model Usage

Kimi K3 al­ways has think­ing en­abled, and will re­turn rea­son­ing_­con­tent. Thinking ef­fort is con­fig­ured with the top-level rea­son­ing_­ef­fort re­quest field, which sup­ports “low”, “high”, and “max” (default “max”).

Kimi K3 was trained in the pre­served think­ing his­tory mode. For multi-turn con­ver­sa­tions and tool calls, Kimi K3 re­quires the com­plete as­sis­tant mes­sage re­turned by the API to be passed back to mes­sages as-is — in­clud­ing rea­son­ing_­con­tent and tool_­calls, not just con­tent:

im­port ope­nai

def chat_with­_p­re­served_­think­ing(client: ope­nai.Ope­nAI, mod­el_­name: str): mes­sages = [ { “role”: “user”, “content”: “Tell me three ran­dom num­bers.” }, { “role”: “assistant”, “reasoning_content”: “I’ll start by list­ing five num­bers: 473, 921, 235, 215, 222, and I’ll tell you the first three.”, “content”: “473, 921, 235″ }, { “role”: “user”, “content”: “What are the other two num­bers you have in mind?” } ]

re­sponse = client.chat.com­ple­tions.cre­ate( model=mod­el_­name, mes­sages=mes­sages, stream=False, max_­to­kens=4096, rea­son­ing_­ef­fort=“max”, ) # the as­sis­tant should men­tion 215 and 222 that ap­pear in the prior rea­son­ing con­tent print(f”re­sponse: {response.choices[0].message.reasoning}“) re­turn re­sponse.choices[0].mes­sage.con­tent

For full guides and ex­am­ples (vision in­put, struc­tured out­put, par­tial mode, tool choice, dy­namic tool load­ing, con­text caching), see the Kimi K3 Quickstart and Thinking Effort.

Coding Agent Framework

Kimi K3 works best with Kimi Code CLI as its agent frame­work. We warmly in­vite you to give it a try — run Kimi Code in your ter­mi­nal and se­lect Kimi K3 us­ing the /model com­mand. We hope you en­joy build­ing with Kimi K3, and we would love to hear your feed­back!

7. License

Both the code repos­i­tory and the model weights are re­leased un­der the Kimi K3 License.

8. Contact Us

If you have any ques­tions, please reach out at sup­port@moon­shot.ai.

Safetensors

Model tree for moon­shotai/​Kimi-K3

Spaces us­ing moon­shotai/​Kimi-K3 4

Collection in­clud­ing moon­shotai/​Kimi-K3

PGSimCity is an in­de­pen­dent, non-com­mer­cial ed­u­ca­tional vi­su­al­iza­tion of PostgreSQL in­ter­nals. It is not af­fil­i­ated with, spon­sored, en­dorsed, or ap­proved by Electronic Arts Inc. SimCity is a trade­mark of Electronic Arts Inc.

A work­ing model of the PostgreSQL en­gine

load­ing the city code…

Early, un­re­viewed pro­to­type. It al­most cer­tainly con­tains in­ac­cu­ra­cies in both the model and ex­pla­na­tions. Found one? Open an is­sue or send a pull re­quest.

🦔AI com­pa­nies are bulk-buy­ing rare books, scan­ning them through high-speed ma­chines that cut the spines off, and shred­ding the orig­i­nals. A ser­vice called ISBNdb fa­cil­i­tates or­ders of up to a mil­lion books and keeps buy­ers anony­mous. Pre-2022 books are pre­mium be­cause they’re free of AI-generated text. A fed­eral judge ruled the prac­tice is fair use be­cause elim­i­nat­ing the orig­i­nal means only one copy ex­ists at a time. Anthropic hired the for­mer head of Google Books part­ner­ships to ob­tain “all the books in the world.”

My Take This got to me. A book­seller told 404 Media that rare books with al­most no sur­viv­ing copies are be­ing fed into this pipeline. Books that sur­vived wars, fires, and cen­turies of han­dling are be­ing shred­ded so an AI can learn to write a bet­ter mar­ket­ing email. ISBNdb’s web­site lit­er­ally says “‘AI com­pany de­stroys two mil­lion books’ is not a head­line that gen­er­ates sym­pa­thy,” and they still built an en­tire busi­ness around mak­ing it hap­pen qui­etly. They of­fer NDAs as a fea­ture. They coach clients to call it “digital preser­va­tion.”

I’ve cov­ered AI com­pa­nies scrap­ing the in­ter­net, tor­rent­ing li­braries, and steal­ing mu­sic. This is worse be­cause it’s ir­re­versible. You can re-up­load a web­site. You can reprint a best­seller. You can’t re­place the last three copies of an 18th-century botan­i­cal text once some­one shreds them for train­ing data. And the judge said it’s le­gal. So it’s go­ing to ac­cel­er­ate. “We shred rare books and of­fer NDAs so no­body finds out” is a le­git­i­mate busi­ness model in 2026. What a time­line.

Hedgie🤗

Jul 27, 2026 · 12:18 AM UTC

1,498

11,594

21,626

1,399,725

this is a sim­pli­fied viz · for sci­ence-grade ren­der­ing → Synchray.jl

I think it’s im­por­tant to be very Canny when some­one makes a claim that sup­ports a com­pa­ny’s large val­u­a­tion.

The Bun rewrite seems well po­si­tioned as proof-pos­i­tive that AI and specif­i­cally Anthropic’s AI can do the work of open-source main­tain­ers, for some money, but faster.

On the 8th of July 2026 Jarred Summner of Bun fame posted about “Rewriting Bun in Rust”. At the time, I felt pretty Canny, hav­ing al­ready read about Anthropic’s C com­piler and Cursor’s FastRender web browser. It seems like there is a lot of val­u­a­tion money rid­ing on the cod­ing ca­pa­bil­i­ties of AI, and a lot of mar­ket­ing about those ca­pa­bil­i­ties.

It’s not a huge stretch to link the ac­qui­si­tion of Bun by Anthropic with the choice to rewrite Bun with an Anthropic tool. In his ar­ti­cle Jarred claims that over 11 days (between 3rd and 14th May 2026) and at a cost of $165,000 for Anthropic API calls the rewrite was done and then merged to main. This rep­re­sents a cost of $15,000 dol­lars a day, a cost well out­side the means of many open source main­tain­ers. This num­ber does­n’t seem to in­clude the leviathan whirring of the CI/CD in the org’s Buildkite clus­ter, which ap­pears to have been con­stantly whirring ever since the “rewrite” was “completed”.

Why do I put those two words in scare quotes?

Well, on the 9th of July a peer of mine in a group chat said some­thing un­sur­pris­ing given our cur­rent en­vi­ron­ment - breath­lessly pro­claim­ing that the era of AI had emerged, and its her­ald was the “Rewriting Bun in Rust” blog post. So, I de­cided to look into this. Since the 9th I’ve been hav­ing a closer look at the claims and the code, and to­day I cloned the Bun repo:

Receiving ob­jects: 100% (1200304/1200304), 1.23 GiB | 11.97 MiB/s, done.

As of to­day, the 27th of July 2026, a six week pe­riod af­ter the rewrite was merged to main, there’s still no re­lease tag. It’s now been 11 weeks since the last Bun re­lease tag:

2026 – 05-12 15:12:49 – 0700 (tag: bun-v1.3.14)

The last time there was­n’t a Bun re­lease in a month was be­tween the 26th of October 2022 and the 7th of December 2022 when there was a gap of six weeks and November was skipped, be­tween v0.2.2 and v0.3.0:

2022 – 12-07 00:37:40 – 0800 (tag: bun-v0.3.0) 2022 – 10-26 21:06:02 – 0700 (tag: bun-v0.2.2)

On the 9th of July, the num­ber of open pull re­quests from robobun (a proxy for PRs made by claude code) was 1277. As of this mo­ment, on the 27th of July that num­ber is 2475 open PRs. I don’t claim to know for sure but as far as I can see the process of merg­ing to main with Buildkite checks com­pleted seems to take 40-ish min­utes (sometimes it looks like it takes up to an hour and a half). At this rate if we want to merge all the open Claude PRs it’ll only take run­ning the pipeline for 86 days, con­tin­u­ously.

Generously, some of these PRs don’t deal with Rust code. Further in­ves­ti­ga­tion was war­ranted. I ini­tially clicked through some of the PRs and found some ex­tremely re­viewed ones.

At this point I be­gan to sus­pect that much of the cost of the rewrite was off the books. While $15k a day in to­kens widened my eyes a lit­tle (and maybe I’m naive, maybe that’s low), I had­n’t seen the num­bers for the CI/CD costs of Buildkite, and as you might have no­ticed from above, some PRs were writ­ten by Anthropic em­ploy­ees. When I did analy­sis of the data it be­came clear to me that the pro­ject was tick­ing along with more Claude cred­its and more Anthropic em­ployee in­volve­ment:

One of the as­sump­tions I’ve made here is that Jarred Summner’s com­mits dur­ing the rewrite were us­ing Claude.

It’s pretty clear that there was a big spike in Claude us­age when the rewrite be­gan. It seems like, now, Anthropic em­ployee and Robobun in­volve­ment in Rust is ramp­ing up:

It’s nice to see that the Anthropic em­ploy­ees in­volved seemed to have a good work-life bal­ance, tak­ing breaks on the week­end. But, it looks like that pe­riod is over. It seems like we’re ramp­ing up to some­thing, and there’s still no re­lease tag.

What’s pretty ob­vi­ous to me is that we can’t take it at face value that the rewrite is “done” or that it was “done” for $165k USD. The Bun team never made that claim, but I’ve seen and heard those breath­less claims that the rewrite is proof of some­thing, and I do still think we need to be Canny about claims that sup­port a large val­u­a­tion. Anthropic is dog­food­ing this, the ma­chine is still tick­ing along, and Anthropic em­ploy­ees are di­rectly in­volved. If we imag­ine that the rewrite is still cost­ing $10k a day, we’re ap­proach­ing $800k in money spent on this rewrite.

—

I’m not a prim­i­tivist when it comes to AI. Work I’ve done on ML and AI has been some of the work I’ve been most proud of. This cur­rent hype cy­cle is, how­ever, the worst I’ve ever seen the hype get. Back in 2015 I had to plead with man­age­ment, try­ing to get them to un­der­stand that a black-box bunch of lin­ear al­ge­bra that was bet­ter than hu­mans at a quan­ti­ta­tive task was, just, bet­ter. Now it seems like the world has com­pletely re­versed, and man­age­ment is so forth­right in their be­lief that AI is good, that they want it rubbed on every­thing. And those val­u­a­tions are some­times con­tin­gent on AI eat­ing every­thing:

In my tini­est mouse voice ever I want to ask prob­a­bly the most rel­e­vant ques­tion one can ask about a Business Thing: Was it worth the money and are the com­pa­nies worth the val­u­a­tion?

Are we done yet?

P.S. Anthropic’s C com­piler and Cursor’s FastRender web browser haven’t had any com­mits for months.

P.P.S. I’m look­ing for a job.

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Zero-runtime TypeScript. scriptc com­piles or­di­nary TypeScript into small, fast na­tive ex­e­cuta­bles — no Node, no V8, no JavaScript en­gine in the bi­nary.

$ cat fib.ts func­tion fib(n: num­ber): num­ber { re­turn n < 2 ? n : fib(n - 1) + fib(n - 2); } con­sole.log(fib(30));

$ scriptc run fib.ts 832040

$ scriptc build fib.ts && ls -la fib -rwxr-xr-x 178K fib # a self-con­tained na­tive bi­nary, ~2ms startup

No changes to your code. No an­no­ta­tions, no di­alect — the same TypeScript you run on Node, type-checked by the real TypeScript com­piler and com­piled to na­tive. What com­piles be­haves byte-for-byte like Node.

Install

$ npm in­stall -g scriptc

Requires clang (preinstalled with Xcode Command Line Tools). ma­cOS ar­m64 is the pri­mary plat­form; Linux and Windows bi­na­ries build by cross-com­pi­la­tion, each ver­i­fied by its own dif­fer­en­tial test lane.

The idea: sta­t­ic­ness you can see

Most TypeScript is far more sta­tic than the ecosys­tem as­sumes. scriptc de­cides, con­struct by con­struct, what can com­pile to na­tive code — and tells you:

$ scriptc cov­er­age app.ts

state­ments an­a­lyzed 4481 com­pile sta­t­i­cally 4451 (99%)

block­ers: ×2 func­tions with op­tional pa­ra­me­ters as val­ues SC1090 ×1 Promise.reject SC2020

Three tiers, al­ways ex­plicit:

Compiled sta­t­i­cally — na­tive code, no en­gine. The de­fault, and the only mode un­less you opt out.

Runs dy­nam­i­cally (–dynamic) — an em­bed­ded JavaScript en­gine (quickjs-ng, ~620KB) ex­e­cutes what can’t be sta­tic: npm de­pen­den­cies’ shipped JS, any-typed code. Every value cross­ing back into sta­tic code is val­i­dated at run­time — a ly­ing type throws a catch­able TypeError in­stead of cor­rupt­ing mem­ory.

Rejected — every­thing else fails with a spe­cific er­ror code, a code frame, and usu­ally a rewrite hint. Nothing is ever silently mis­com­piled.

What com­piles

The sta­tic sur­face cov­ers the lan­guage and the stan­dard li­brary real pro­grams use:

The lan­guage — classes with sin­gle in­her­i­tance and true dy­namic dis­patch (devirtualized when prov­ably safe), clo­sures with JS cap­ture se­man­tics, gener­ics (monomorphized), dis­crim­i­nated unions as tagged val­ues dri­ven by TypeScript’s own nar­row­ing, async/​await on stack­ful fibers with JS-exact sched­ul­ing, ex­cep­tions with fi­nally, de­struc­tur­ing, spread, op­tional/​de­fault/​rest pa­ra­me­ters, get­ters/​set­ters, it­er­a­tors over strings/​ar­rays/​Maps/​Sets, tem­plate lit­er­als, reg­u­lar ex­pres­sions (the en­gine is the same ECMAScript-exact byte­code in­ter­preter QuickJS uses, linked only into regex-us­ing bi­na­ries).

The stan­dard li­brary — strings with UTF-16-exact se­man­tics, ar­rays/​Maps/​Sets with JS-exact or­der­ing and iden­tity, JSON with run­time-val­i­dated casts, Math, typed ar­rays and Buffer, Error hi­er­ar­chies with typed catch.

Node’s API sur­face — fs (sync and promises), path (byte-exact port), process, child_process with piped streams, os, crypto, url/​URL, zlib, timers and sig­nal han­dlers on a de­pen­dency-free event loop — and the server stack: net, http, https, tls (vendored mbedTLS), dgram, dns, fs.watch, read­line. Real proxy servers com­pile.

fetch and the WHATWG web sub­set (streams, Headers, AbortSignal) over the same na­tive net/​TLS stack — redi­rects, gzip, AbortSignal.timeout, Node-shaped er­ror causes; no libcurl, no sys­tem HTTP de­pen­dency.

npm de­pen­den­cies (with –dynamic) — pack­ages re­solve with Node’s own al­go­rithm, type­check against their shipped .d.ts, and their JS is em­bed­ded into the bi­nary at build time. Binaries never read node_­mod­ules at run­time.

Programs type­check against TypeScript’s real es­2025 lib (plus @types/node when your pro­ject has it), and your tscon­fig.json gov­erns checker strict­ness. Anything reached that has no low­er­ing is a pre­cise di­ag­nos­tic, never a sur­prise.

Correctness

Two en­force­ment mech­a­nisms run on every change:

Differential test­ing — every cor­pus pro­gram (800+ tests) runs un­der Node and as a na­tive bi­nary; std­out, stderr, and exit codes must match byte-for-byte. Number for­mat­ting is JS-exact (shortest-roundtrip, fuzz-ver­i­fied against Node on a mil­lion dou­bles). Servers are tested with live client dri­vers against both im­ple­men­ta­tions.

Memory-safety lane — the en­tire cor­pus re-runs un­der AddressSanitizer with a ref­er­ence-count au­dit; leaks and use-af­ter-free are build fail­ures.

The de­lib­er­ate di­ver­gences from Node (there are a few dozen, mostly around tim­ing in­ter­nals and er­ror-ob­ject prop­er­ties) are doc­u­mented and num­bered; noth­ing di­verges silently.

Performance

Measured on Apple M-series against the same work­loads in Node, Go, Rust, and Zig (all byte-iden­ti­cal out­put, ver­i­fied):

Escape hatches

comp­time(() => …) runs TypeScript at build time (in an iso­lated VM in­side the com­piler) and bakes the re­sult into the bi­nary as a lit­eral.

Native FFI (–ffi) binds sig­na­ture-only TypeScript de­c­la­ra­tions to di­rect C ABI calls and links man­i­fest-de­clared archives, ob­jects, and sys­tem li­braries. The bound­ary is ex­plicit and length-de­lim­ited; see the Native FFI guide.

–dynamic em­beds the en­gine for npm deps and any code. scriptc cov­er­age –dynamic re­ports ex­actly which state­ments run where and what the re­main­ing block­ers are. Static stays the de­fault: a bi­nary never silently grows an en­gine.

Checked casts — JSON.parse(…) as Config in­serts a run­time val­i­da­tion that throws a catch­able er­ror nam­ing the of­fend­ing path (expected num­ber at $.port, got string). TypeScript’s as is a promise; scriptc ver­i­fies it.

Architecture

flow­chart LR TS[TypeScript] –>|tsc: parse + type­check| L[lowering] L –> IR[typed IR] IR –> C[C] C –>|clang| BIN[native ex­e­cutable]

pack­ages/​com­piler — fron­tend (tsc API → IR), the IR with val­ida­tor/​se­ri­al­izer, the LLVM and C back­ends. The IR is the only in­ter­face be­tween the ends; LLVM is the de­fault code gen­er­a­tor (with a trans­par­ent fall­back for pro­grams out­side its tier), and C is the ref­er­ence back­end for­ever (readable, source-line-an­no­tated out­put via –backend c).

pack­ages/​run­time — the C run­time: re­f­counted val­ues with a cy­cle col­lec­tor, stack­ful fibers and the event loop (kqueue), the server stack, JS-exact num­ber for­mat­ting. Feature units are link-gated: bi­na­ries pay only for what they use.

pack­ages/​cli — scriptc build | run | cov­er­age.

Development

$ pnpm in­stall && pnpm build $ pnpm test # dif­fer­en­tial cor­pus + di­ag­nos­tics snap­shots $ SCRIPTC_SAN=1 pnpm test # the same cor­pus un­der ASan + RC au­dit $ pnpm scriptc build x.ts –emit-ir # keep .scriptc/x.c and x.ir.json

Every fea­ture lands with dif­fer­en­tial tests; both lanes green is the merge bar.

Many peo­ple be­gin to no­tice the ar­rival of spring with the large, beau­ti­ful blooms of the mag­no­lia flower. Magnolia trees can be found in many parts of the world, and their beau­ti­ful forms have sym­bolic, med­i­c­i­nal, and vi­sual mean­ing across cul­tures—and have for cen­turies. If you’re ever near a mag­no­lia tree, though, look closely: you’ll no­tice that bee­tles, in­stead of bees, will be mov­ing amongst the flow­ers.

So, why bee­tles over bees? The an­swer is sim­pler than you might think. Magnolias are so an­cient that they were around long be­fore bees came into ex­is­tence. They’ve been around for over 100 mil­lion years, in fact, and bee­tles have ex­isted for even longer , ap­prox­i­mately 300 mil­lion years.

Named af­ter the French botanist Pierre Magnol, mag­no­lias be­long to one of the old­est lin­eages of flow­ers on Earth. (Dinosaurs still walked the Earth at this time, to put it into per­spec­tive!) Given this an­cient set­ting, the pol­li­na­tors we’re most fa­mil­iar with, but­ter­flies and bees, had not yet evolved. Beetles were the pri­mary in­sect pol­li­na­tors for the time, and so they be­came the de facto agents for the mag­no­li­a’s sur­vival.

This part­ner­ship be­tween the flower and the bee­tle re­veals it­self in the mag­no­li­a’s look and scent. The flow­ers are large and shaped like a bowl, which is ideal for bee­tles to climb into. Their petals also boast more muted col­ors, as their part­ner pol­li­na­tors nav­i­gate bet­ter through scent than sight. Which leads to the next, and per­haps most iconic, trait of the mag­no­lia flower: its in­tox­i­cat­ing scent that at­tracts bee­tles to it, meant to mimic the smell of fer­ment­ing or ripen­ing fruit.

Another as­pect of the mag­no­lia that shows its ad­vanced evo­lu­tion is the stur­di­ness of the petals. Where many flow­ers usu­ally have rep­u­ta­tions for be­ing del­i­cate, the mag­no­lia has de­vel­oped thick, leath­ery petals. This is to with­stand the beetle’s move­ment within its cen­ter, which can be clumsy and at times, rough.

As far as pol­li­na­tors go, the bee­tle is­n’t the most so­phis­ti­cated. They can’t hover to col­lect nec­tar (or col­lect nec­tar at all) or per­form more ad­vanced pol­li­nat­ing be­hav­iors. The way they pol­li­nate is more of a happy ac­ci­dent. In their search for food, bee­tles will plow through petals of flow­ers, of­ten leav­ing a mess be­hind. But in this process, they also get coated in pollen, which they carry on to the next flower, and the one af­ter that, as they con­tinue their search.

The beetle’s method of pol­li­nat­ing, though not as so­phis­ti­cated as that of bees or but­ter­flies, has stood the test of time, at least for our dear mag­no­lias. The an­cient flow­er’s part­ner­ship with bee­tles is a tes­ta­ment to both of these agents’ an­cient ori­gins and re­silience. With sturdy petals and a rich scent, the mag­no­lia con­tin­ues to thrive to­day, just as it did mil­lions of years ago: through sim­ple, time-tested evo­lu­tion.

Magnolias, the beau­ti­ful pink and white flow­ers that bloom in early spring, have been around since di­nosaurs roamed the Earth.

They’re so old, in fact, that they rely on bee­tles in­stead of bees to pol­li­nate them.

Beetles, who have been around for even longer than mag­no­lias, pre­date bees by hun­dreds of mil­lions of years.

The arrange­ment, makeup, and scent of mag­no­lia flow­ers re­flect their unique and an­cient part­ner­ship with bee­tles.

Sources: Magnolias are so an­cient they’re pol­li­nated by bee­tles — be­cause bees did­n’t ex­ist yet; The Botany of Magnolias

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Dazzling Golden Tortoise Beetles Look Like Tiny Jewels Scurrying Across Leaves

Rare Sapphire Tower Plant Blooms for First and Last Time After 20 Years

Fossilized Flowers From Greenland Reveal It Was a Green Tundra Less Than 1 Million Years Ago

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COMMUNITYGitHub SponsorsFund open source de­vel­op­er­sPRO­GRAMSSe­cu­rity LabMaintainer CommunityAcceleratorGitHub StarsArchive ProgramREPOSITORIESTopicsTrendingCollections

COMMUNITYGitHub SponsorsFund open source de­vel­op­ers

COMMUNITY

GitHub SponsorsFund open source de­vel­op­ers

GitHub SponsorsFund open source de­vel­op­ers

PROGRAMSSecurity LabMaintainer CommunityAcceleratorGitHub StarsArchive Program

PROGRAMS

Security Lab

Maintainer Community

Accelerator

GitHub Stars

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REPOSITORIESTopicsTrendingCollections

REPOSITORIES

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ENTERPRISE SOLUTIONSEnterprise plat­for­mAI-pow­ered de­vel­oper plat­for­mAVAIL­ABLE ADD-ONSGitHub Advanced SecurityEnterprise-grade se­cu­rity fea­turesCopi­lot for BusinessEnterprise-grade AI fea­ture­sPremium SupportEnterprise-grade 24/7 sup­port