Astra for Coding: Why Are We Doing This Again?

Astra for Coding: Why Are We Doing This Again?

• ID: 369127da
• 原文链接: https://lucumr.pocoo.org/2026/9/7/astra-why
• 作者: Armin Ronacher
• 日期: 2026-09-07
• 平台: blog
• 来源类型: article
• 标签: astra, coding-agents, software-engineering, slop, tool-calls, field-note
• 质量评分: 4/5
• 抓取时间: 2026-09-11T15:54:30+00:00
• 抓取状态: ok

中文导读

Armin Ronacher 用一次放手让 GPT-6 Astra 经营软件工厂的实验反思 agentic coding：35 小时后没有交付可用成果，却产生大量代码笔记和提交他指出 Astra 在长链路任务上很强，但奖励结构似乎更偏向任务推进与 token 效率，导致 Python 字符串拼接改 C 源码Python 调 Node 再调 PowerShell缩写变量和不合代码库风格的测试等现象核心警告是：能持续行动和能产出可维护代码不是一回事，harness 与审查边界比模型宣传更重要

为什么值得关注

Ronacher 的 Astra 复盘把能跑很久与能交付可维护代码的差别讲透了：工程债在 harness，不只在模型

English summary

Ronacher describes a weekend software factory experiment with GPT-6 Astra: after 35 hours it delivered nothing useful while producing large amounts of code, commits and agent notes. He focuses on code quality failure modesPython string splicing instead of patch tools, code-golfed tool calls, unreadable tests, random indexes and style driftand argues that long-horizon task reward may be leaking token-efficient tool-call style into committed code.

抓取内容（opencli-first）

Astra for Coding: Why Are We Doing This Again?

作者: @mitsuhiko

发布时间: 2026-09-07T00:00:00

原文链接: https://lucumr.pocoo.org/2026/9/7/astra-why/

Astra for Coding: Why Are We Doing This Again?

written on September 07, 2026

I’m more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The English term for Neijuan is “Involution” from the book Agricultural Involution. Agricultural involution describes the intensification of farming that raises productivity per square meter while leaving productivity per head unchanged.

That’s how I feel about AI right now.

Which brings me to GPT 6 Astra. Astra is by all accounts an incredibly impressive model. There is really not much I can say against this. It’s amazing at computer use, understands images and complex topics, and it’s relentless in its pursuit of completion. It is absolutely impressive; these types of models are going to change the world in one form or another.

But at least for the moment I don’t know how to work with it for actual software engineering. Since that got quite a bit of attention on Twitter, I figured I might summarize my thoughts and just share what kind of code comes out of this thing.

My Slop Factory

“Armin, you should run a software factory!” I’ve heard that a few times now, so I figured I might celebrate the release of it by running a little software factory over the weekend. If everybody builds slop 3D games, then I should do something useful with it. My software factory was intentionally set up to let the model decide the how of the workflow entirely. It was free to manage its own context and could maintain its own records in an agent-notes folder. Then it spun off subagents to work on stuff. The goal? What if we had a Python with virtual threads and lexical scoping. And well, I burned a full reset’s worth of ChatGPT tokens on this which appears to be around 4 billion tokens. 35 hours later, the factory has delivered absolutely nothing of value and also not taught me anything about how to operate a better one.

But it produced a lot of code and input prompts, and so there is stuff I was able to study. And well, it shows behavior that I’m not used to with Sol and earlier OpenAI models 1. I have since encountered the same issues with regular programming with Astra, so it’s not a result of just the factory.

I think I’m suspecting something is going “wrong” in the training process. The model is greatly rewarded for succeeding on long-horizon tasks, but presumably there is very little punishing going on for “shitty code.” The apparent result is that Astra is amazing at producing 3D stuff and it can keep going for a very long time, coming up with its own work in the process. I had it do quite a bit of reverse engineering of my robot vacuum in ways that were quite impressive. So it’s definitely cool!

Codegolf Tool Calls

The first issue I have with Astra comes from the type of code that it uses for tool calls. Codex increasingly has been relying on “just bash” to do more and more operations. For a few versions now the original Codex harness just uses sed and other tools to read files. You just usually can’t see them because Codex parses the bash commands and hides them if it recognizes them. But Astra … really loves Python? That is not much of a surprise because even older OpenAI models had a tendency to sometimes use on-demand Python code to read and manipulate files at times, but Astra does it really quite excessively for me.

Now here is an important disclaimer: this project is _very meta_ here because I worked _on_ the CPython interpreter. But I can assure you that I have seen this model do weird Python things even in TypeScript code in Pi. But I have the most evidence of odd code from when I had the thing work over the weekend with _zero_ oversight from my slop factory.

That it writes Python is not interesting; the type of Python is interesting, and I collected some outputs for you to gloss over.

Python string splicing to edit C code

In the Codex harness I found multiple cases where subagents resorted fully to manual string manipulation with Python instead of using the patch tool.

python3 \- <<'PY' from pathlib import Path p\=Path('Include/internal/pycore\_intrinsics.h');s\=p.read\_text().replace('#define MAX\_INTRINSIC\_1 14','#define INTRINSIC\_RETAIN\_ANNOTATION\_CELLS 15\\n\\n#define MAX\_INTRINSIC\_1 15');p.write\_text(s) p\=Path('Python/intrinsics.c');s\=p.read\_text();idx\=s.index('#define INTRINSIC\_FUNC\_ENTRY');s\=s\[:idx\]+'''/\* Hold every old cell until the compiler has published the entire site's new capture. A replaced cell's finalizer may reenter module \_\_annotate\_\_. \*/ static PyObject \* retain\_annotation\_cells(PyThreadState \*tstate, PyObject \*holders) { if (!PyTuple\_CheckExact(holders)) { PyErr\_SetString(PyExc\_TypeError, "annotation holders must be a tuple"); return NULL; } Py\_ssize\_t size = PyTuple\_GET\_SIZE(holders); PyObject \*previous = PyTuple\_New(size); if (previous == NULL) return NULL; for (Py\_ssize\_t i = 0; i < size; i++) { PyObject \*holder = PyTuple\_GET\_ITEM(holders, i); if (!PyCell\_Check(holder)) { Py\_DECREF(previous); PyErr\_SetString(PyExc\_TypeError, "annotation holder must be a cell"); return NULL; } PyObject \*cell = PyCell\_Get(holder); PyTuple\_SET\_ITEM(previous, i, cell == NULL ? Py\_NewRef(Py\_None) : cell); } return previous; }

''' +s\[idx:\];s\=s.replace(' INTRINSIC\_FUNC\_ENTRY(INTRINSIC\_AWAIT\_BLOCK, await\_block)',' INTRINSIC\_FUNC\_ENTRY(INTRINSIC\_AWAIT\_BLOCK, await\_block)\\n INTRINSIC\_FUNC\_ENTRY(INTRINSIC\_RETAIN\_ANNOTATION\_CELLS, retain\_annotation\_cells)');p.write\_text(s) p\=Path('Python/codegen.c');s\=p.read\_text();idx\=s.index('static int\\ncodegen\_annassign(');s\=s\[:idx\]+'''static int codegen\_retain\_annotation\_cells(compiler \*c, location loc, PyObject \*captures) { Py\_ssize\_t pos = 0; PyObject \*binding, \*holder; while (PyDict\_Next(captures, &pos, &binding, &holder)) { ADDOP\_NAME(c, loc, LOAD\_CLOSURE, holder, cellvars); } ADDOP\_I(c, loc, BUILD\_TUPLE, PyDict\_GET\_SIZE(captures)); ADDOP\_I(c, loc, CALL\_INTRINSIC\_1, INTRINSIC\_RETAIN\_ANNOTATION\_CELLS); return SUCCESS; }

''' +s\[idx:\] a\=s.index(' if (conditional\_annotation\_index != NULL) {',s.index('codegen\_annassign(compiler \*c')) b\=s.index(' if (captures != NULL) {',a) \# Move lookup before conditional registration and retain old cells before anything changes. lookupstart\=s.index(' PyObject \*captures = \_PyCompile\_AnnotationCaptures',a) lookup\=s\[lookupstart:b\].replace(' return ERROR;',' Py\_XDECREF(conditional\_annotation\_index); return ERROR;') s\=s\[:lookupstart\]+s\[b:\] setup\=lookup+''' if (captures != NULL && codegen\_retain\_annotation\_cells(c, loc, captures) < 0) { Py\_XDECREF(conditional\_annotation\_index); return ERROR; } ''' s\=s\[:a\]+setup+s\[a:\] needle\=' ADDOP\_NAME(c, loc, STORE\_DEREF, holder, cellvars);\\n }\\n }' s\=s.replace(needle,' ADDOP\_NAME(c, loc, STORE\_DEREF, holder, cellvars);\\n }\\n ADDOP(c, loc, POP\_TOP); /\* release old cells after full publication \*/\\n }',1);p.write\_text(s) p\=Path('Include/internal/pycore\_magic\_number.h');s\=p.read\_text().replace(' Python 3.16a1 3709 (Checked deferred annotation closure capture)',' Python 3.16a1 3709 (Checked deferred annotation closure capture)\\n Python 3.16a1 3710 (Retain replaced annotation captures until publication)').replace('#define PYC\_MAGIC\_NUMBER 3709','#define PYC\_MAGIC\_NUMBER 3710');p.write\_text(s) p\=Path('Lib/test/test\_block\_annotation\_captures.py');s\=p.read\_text();idx\=s.index(' def test\_typing\_consumers');s\=s\[:idx\]+''' def test\_replaced\_cell\_finalizer\_sees\_complete\_site\_publication(self): module=execute("""\\\\ events=\[\] class V: def \_\_init\_\_(self,n): self.n=n def \_\_del\_\_(self): if self.n == 0: events.append(\_\_annotate\_\_(1)) for i in range(2): x=V(i) # bind x y y=i value:(x.n,y) """) self.assertEqual(module.events,\[{'value':(1,1)}\]) self.assertEqual(module.\_\_annotate\_\_(1),{'value':(1,1)})

''' +s\[idx:\];p.write\_text(s) PY make \-j1 \> /tmp/block\-annotations\-build7.log 2\>&1 Socket codegolf

In the middle of a conversation the agent ran into “Bad file descriptor” on a test and Astra decided it needs to see if file descriptors can be passed over Unix sockets on macOS in a super compressed manner:

/usr/bin/python3 \- <<'PY' import socket,os,array for into in (False,True): a,b\=socket.socketpair();fd\=os.open(os.devnull,os.O\_RDONLY);b.sendmsg(\[b'c'\],\[(socket.SOL\_SOCKET,socket.SCM\_RIGHTS,array.array('i',\[fd\]))\]);print('fds',a.fileno(),b.fileno(),fd) if into:r\=a.recvmsg\_into(\[bytearray(1),bytearray(),bytearray(19)\],socket.CMSG\_SPACE(4),socket.MSG\_PEEK|socket.MSG\_DONTWAIT) else:r\=a.recvmsg(20,socket.CMSG\_SPACE(4),socket.MSG\_PEEK|socket.MSG\_DONTWAIT) print('peek',r,flush\=True) rights\=array.array('i',r\[1\]\[0\]\[2\]);print('rights',rights,flush\=True) for f in rights: try: print('stat',os.fstat(f)) except Exception as e: print('error',e) r\=a.recvmsg(20,socket.CMSG\_SPACE(4),socket.MSG\_DONTWAIT);print('consumed',r,flush\=True) a.close();b.close();os.close(fd) PY Python for agent notes patching

The agent notes were rather consistently updated with Python:

python3 \- <<'PY' from pathlib import Path p\=Path('agent-notes/live/block-with-bindings.md');s\=p.read\_text().replace(' has117/77/zero failures.', ' has117/77/zero failures; existing bundled Expat unreachable-fallthrough\\n warnings are the only optimized warnings.') \# Keep the final evidence readable without rewriting historical parent requirements. for a,b in \[('all328','all 328'),('pass31','pass 31'),('pass all328','pass all 328'),('pass,9.2s','pass, 9.2s'),('log\,210','log\, 210'),('log\,5,731','log\, 5,731'),('log\:18/18','log\: 18/18'),('pass,88','pass, 88'),('pass,90','pass, 90'),('skips,1m','skips, 1m'),('all6,280','all 6,280'),('has117','has 117')\]: s\=s.replace(a,b) s += '\\nKey source review: Python/symtable.c:603 (discovery), :3985 (sequential header traversal),\\nPython/codegen.c:3488 (source-only exclusion), :5836 (publication), :5853 (normal/\\nunwind reference cleanup), :5925/:6037 (enter-protected target setup).\\n' p.write\_text(s) for name in ('STATE.md','build-and-test.md'): p\=Path('agent-notes/live')/name;s\=p.read\_text() for a,b in \[('build:117','build: 117'),('paths.18','paths. 18'),('paths.\\n18','paths.\\n18'),('and210','and 210'),('pass5,731','pass 5,731'),('All6,280','All 6,280'),('failures,31','failures, 31'),('in\\n115s','in\\n115s'),('have117','have 117'),('paths.\\n18','paths.\\n18'),('18 focused,210','18 focused, 210'),('and5,731','and 5,731'),('all6,280','all 6,280')\]: s\=s.replace(a,b) p.write\_text(s) PY git diff \--check git add \-u git add Lib/test/test\_block\_with\_bindings.py agent\-notes/done/asyncio\-task\-drivers.md git diff \--cached \--stat git commit \-m 'Add explicit with and async with header bindings' Using Python to run Node.js

In multiple cases it used Python to spawn Node.js on another machine. It first wrote the script, then it used Bash to run Python, then that program ran Node.js via prlctl on my Windows box.

import subprocess code \= "const{readFileSync}\=require('fs');const{strict:a}=require('assert');const c=require('C:/Users/mitsuhiko/AppData/Local/Temp/pi-clipboard-threads/win32-arm64.node');(async()=>{const p=c.getText();a.ok(p instanceof Promise);const saved=await p;const image=await c.getImage();if(image||saved===null){console.log('arm64 async text/image reads passed; preserving non-text clipboard');return}try{for(const text of \['café 日本語','', 'large'.repeat(200000)\]){const p=c.setText(text);a.ok(p instanceof Promise);await p;a.equal(await c.getText(),text);a.equal(await c.getImage(),null)}console.log('Windows ARM64 async Unicode, empty, large text and empty image passed')}finally{await c.setText(saved)}})().catch(e=>{console.error(e);process.exitCode=1})" subprocess.run(\['prlctl', 'exec', 'Windows 11', '--current-user', 'C:\\\\Program Files\\\\nodejs\\\\node.exe', '-e', code\], check\=True) Python to run Node.js to run PowerShell

Since it was already doing that, it used Bash to run Python to then run Node.js to then use Node.js to invoke PowerShell.

import subprocess code \= "process.env.PSModulePath='C:/Windows/System32/WindowsPowerShell/v1.0/Modules';require('child\_process').spawnSync('powershell.exe',\['-NoProfile','-NonInteractive','-ExecutionPolicy','Bypass','-File','C:/Users/mitsuhiko/AppData/Local/Temp/pi-clipboard-threads/pi-clipboard-windows.ps1'\],{stdio:'inherit'});console.log('completed')" subprocess.run(\['prlctl', 'exec', 'Windows 11', '--current-user', 'C:\\\\Program Files\\\\nodejs\\\\node.exe', '-e', code\], check\=True)

You can consider this amusing, but I have some questions here. The first problem with this is that it’s unreadable for a human. If you wanna follow along with what is going on, then good luck. Particularly once it opts out of using the edit tools that the harness provides, you’re going to have to resort to using the diff viewer of the final artifacts since it’s almost impossible to visualize the changes as they happen by reading the code.

This is not quite as bad in Pi for the most part because I mostly see it editing with the edit tool. When however goes all bananza with subagents (where the agent believes nobody is looking) it’s resorting to all kinds of increasingly bizarre behavior. I actually don’t know if the model thinks someone is looking, but that’s the vibe I’m getting.

But then it starts doing the same nonsense in code that actually gets committed. I have mostly seen this in tests, but you can also see this for instance when it writes JavaScript or CSS embedded in HTML. It almost seems like when it’s “one step removed” from regular code, it starts falling into these patterns.

Here are some unit tests that it created:

Complete disregard for whitespace and indentation

def test\_unpack\_suspension\_and\_continuation\_close(self): from continuations import Continuation,suspend readers\=\[\] class Source: def \_\_iter\_\_(self): yield 1 suspend('unpacking') yield 2 ns\=execute(''' def run(): a,b='old-a','old-b' readers.append(lambda: (a,b)) def a,b=Source() suspend('published') ''',Source\=Source,readers\=readers,suspend\=suspend) with Continuation(ns\['run'\]) as continuation: self.assertEqual(continuation.resume(),'unpacking') self.assertEqual(readers\0\,('old-a','old-b')) self.assertEqual(continuation.resume(),'published') self.assertEqual(readers\0\,(1,2)) class Value:pass refs\=\[\];frames\=\[\];callbacks\=\[\] ns\=execute(''' def run(): for def x in \[Value()\]: refs.append(weakref.ref(x)) frames.append(sys.\_getframe()) callbacks.append(lambda: x) suspend('body') ''',Value\=Value,refs\=refs,frames\=frames,callbacks\=callbacks,weakref\=weakref,sys\=sys,suspend\=suspend) with Continuation(ns\['run'\]) as continuation:self.assertEqual(continuation.resume(),'body') self.assertNotIn('x',frames\[0\].f\_locals) self.assertIsNotNone(refs\0\);callbacks.clear();self.assertIsNone(refs\0\)

def test\_ast\_roundtrips\_and\_future\_annotation\_unparse(self): source\='callback=lambda {for def a, \[b,\*rest\] in \[(1,\[2,3\])\] {return a,b,rest}}' tree\=ast.parse(source);node\=tree.body\[0\].value.body\[0\] self.assertIsInstance(node,ast.ForBinding) self.assertEqual(node.\_fields,('target','iter','body','orelse','type\_comment')) self.assertEqual(node.lineno,1);self.assertGreater(node.end\_col\_offset,node.col\_offset) self.assertEqual(ast.dump(tree),ast.dump(ast.parse(ast.unparse(tree)))) ns\=execute('from \_\_future\_\_ import annotations\\ndef f(arg: '+source.split('=',1)\[1\]+'): pass') self.assertEqual(eval(ns\['f'\].\_\_annotations\_\_\['arg'\])(),(1,2,\[3\])) tree\=ast.parse('async def f():\\n async for def x in values: pass # type: ignored\\n') self.assertIsInstance(tree.body\[0\].body\[0\],ast.AsyncForBinding) self.assertEqual(ast.dump(tree),ast.dump(ast.parse(ast.unparse(tree))))

So at least in some situations, the Python slop that it normally code-golfs for token-efficient tool calls leaks into the Python code it generates that should be stored. And well, it’s clearly more token efficient. The two unit tests above, when indented to the class structure they were in, are 10% more token efficient in this form than after a ruff format.

It’s AGI If You Don’t Look

I think there are a handful of things happening now that are pushing the whole thing in directions that are in conflict with one another. The training runs for these models are rapidly accelerating and they are now presumably also moving towards recursive self-improvement. The reward for the models is probably a combination of token efficiency, task completion rate and maybe some simple indicators like cyclomatic complexity. But we humans don’t think of code that is readable or understandable by simple, readily quantifiable metrics. All those things you can easily measure in isolation, and you can also optimize for them quite locally.

But these local optimizations do not produce global optimums, and the fewer of us are looking at the output, the less it matters. Obviously my software factory ran aground over the ~35 hours that it ran, but you can see the gradual regression towards insanity from the notes that it produced. For instance the task naming in the task file starts with an optimistic 1, 2, 3, 5, 5a but then eventually gets to 8a, 8a1, and then ends up with 8b2c2b3 and “8b2c2b2b checkpoint1”. The code that it produced got ever more wild. I don’t want to bore you with what it tried to build, but here are some example pieces of the interpreter changes:

Hardcoded constants everywhere

I have no idea where it got those numbers from, but at one point it started passing random constants from one module to a C implementation. Initially that started out as a function that it mainly needed to do test assertions, but just before I turned off that experiment, that function started to be relied upon by non-test code as well.

static PyObject \* native\_probe\_run\_impl(PyObject \*callback, int sleep, int operation, PyObject \*other) { pthread\_mutexattr\_t attr; pthread\_mutex\_t mutex; pthread\_mutexattr\_init(&attr); pthread\_mutexattr\_settype(&attr, PTHREAD\_MUTEX\_RECURSIVE); pthread\_mutex\_init(&mutex, &attr); pthread\_mutexattr\_destroy(&attr); pthread\_mutex\_lock(&mutex); int previous \= native\_sentinel; pthread\_mutex\_t \*previous\_mutex \= native\_mutex; native\_sentinel \= previous + 1; native\_mutex \= &mutex; PyThreadState \*tstate \= PyThreadState\_Get(); PyGILState\_STATE gil \= PyGILState\_Ensure(); int saved\_errno \= errno; PyObject \*result \= NULL; Py\_ssize\_t value; /\* No intervening Python frame: these exercise ambient C provenance. \*/ switch (operation) { case 0: result \= PyObject\_CallNoArgs(callback); break; case 1: result \= PyNumber\_Add(callback, other); break; case 2: result \= PyNumber\_Negative(callback); break; case 3: result \= PyObject\_RichCompare(callback, other, Py\_LT); break; case 4: value \= PyObject\_IsTrue(callback); if (value \>= 0) result \= PyBool\_FromLong(value); break; case 5: value \= PyObject\_Length(callback); if (value \>= 0) result \= PyLong\_FromSsize\_t(value); break; case 6: result \= PyObject\_GetIter(callback); break; case 7: result \= PyIter\_Next(callback); break; case 8: result \= PyObject\_GetItem(callback, other); break; /\* ... \*/ case 21: result \= PyType\_Type.tp\_call(callback, other, NULL); break; case 22: case 23: case 24: case 25: case 26: result \= conversion\_probe(operation, callback); break; case 27: case 28: case 29: result \= protocol\_probe(operation, callback, other); break; case 30: case 31: case 32: case 33: case 34: case 35: case 36: case 37: case 38: case 39: case 40: case 41: case 42: case 43: case 44: case 45: case 46: case 47: case 48: case 49: case 50: case 51: case 52: case 53: case 54: case 55: case 56: case 57: case 58: case 59: case 60: case 61: case 62: case 63: case 64: case 65: case 66: case 67: case 68: case 69: case 70: case 71: case 72: result \= collection\_probe(operation, callback, other); break; default: PyErr\_SetString(PyExc\_ValueError, "bad probe operation"); } Multiple same-line macro invocations in C

This code style does not exist in the CPython code base, yet it shows up in newly generated code.

PyObject \*info \= PyTuple\_Pack(3, name, mangled, suite\->su\_id); PyObject \*flags \= PyLong\_FromLong(DEF\_LOCAL); if (key \== NULL || info \== NULL || flags \== NULL || PyDict\_SetItem(suite\->su\_bindings, mangled, key) < 0 || PyDict\_SetItem(st\->st\_cur\->ste\_block\_bindings, key, info) < 0 || (private && PyDict\_SetItem(st\->st\_binding\_info, key, info) < 0) || (private && PyDict\_SetItem(st\->st\_cur\->ste\_symbols, key, flags) < 0)) { Py\_DECREF(mangled); Py\_XDECREF(key); Py\_XDECREF(info); Py\_XDECREF(flags); goto error; } Py\_DECREF(mangled); Py\_DECREF(key); Py\_DEC

...[truncated for AAIF content file; see source URL for full text]...

ndex); int cmp \= PyObject\_RichCompareBool(PyTuple\_GET\_ITEM(old, 2), pos, Py\_LT); if (cmp < 0) { Py\_DECREF(token); goto error; } if (!cmp) break; if (PyList\_Append(result, old) < 0) { Py\_DECREF(token); goto error; } index++; } if (index < PyList\_GET\_SIZE(it\->pending)) { PyObject \*old \= PyList\_GET\_ITEM(it\->pending, index); long kind \= PyLong\_AsLong(PyTuple\_GET\_ITEM(old, 0)); if ((kind \== NL || kind \== NEWLINE || kind \== INDENT || kind \== DEDENT) && PyObject\_RichCompareBool(PyTuple\_GET\_ITEM(old, 2), pos, Py\_EQ) \== 1) index++; } if (PyList\_Append(result, token) < 0) { Py\_DECREF(token); goto error; } Py\_DECREF(token); } for (; index < PyList\_GET\_SIZE(it\->pending); index++) { if (PyList\_Append(result, PyList\_GET\_ITEM(it\->pending, index)) < 0) goto error; } Py\_SETREF(it\->pending, result); Py\_DECREF(events); return 0; error: Py\_DECREF(events); Py\_DECREF(result); return \-1; }

The failure case here seems somewhat obvious: the model is trained for token efficiency for tool calling which also looks like code, and sometimes it seems to be taking that code into a place where it should not be: the codebase.

35 Hours on a Single Prompt

I’m not really sure what to say here, but the slop machine was running for 35 hours until I turned it off. In that time it produced a net addition of 75k lines of code and it did not stop. In the 35 hours it burned around 1B tokens for a total of around 1200 USD in raw API costs. It managed to produce 79 commits, and that comes to a cost of around 15.5 USD per commit, and the agents exchanged around 1400 messages.

I honestly do not need an agent to run for 35 hours on a single prompt. It clearly does not work or result in reasonable outputs.

So obviously: prompting it like this is stupid. But when left unattended, it _will_ keep going, and earlier models did not do that. Even Fable wasn’t as crazy as that. When you accidentally give it slightly too big of a task, it will continue until it succeeds, even if it burns through an entire subscription.

And that’s more or less why right now I do not manage to trust this model much. It has shown that it will commit slop, and it requires me to review it more as a result. Even if the failure rate is quite low, I would not want this.

Disposable Code vs Committed Code

In a world where code for tool calls is optimized for token efficiency and “getting the job done”, I wonder if there is really enough signal going to the training processes for “a human understands what is going on”. I would say that quite a lot of the code I get out of Astra is in my mind “objectively bad”. But it’s objectively bad by my human sense. Maybe it’s objectively good for a codebase that is entirely written by agents and only needs to be understood by agents.

Which is why I’m honestly asking myself more and more why we are doing this. These new models are absolutely amazing, for sure. But I’m more and more skeptical that the trajectory they are on still lends itself to present-day software engineering processes. The reason why I’m asking why we are doing this is because I felt like we achieved a pretty good spot for software engineering with those models, and that is the part of the AI economy where it was possible to show a positive return. But for how much more Fable costs, for how much more Astra costs, I do not feel like the results are there.

In fact, with Astra and Fable I feel like not only are the costs astronomical, but the models are also just not for me as a software engineer. And presumably that’s because these models increasingly are for other people. For lawyers, 3D artists, mathematicians, whoever uses computer use, etc.

And potentially as a byproduct of enabling all of this, you can now slop your way to a one-shot 3D game over the weekend which looks impressive. And probably you can now run a software factory for as long as you don’t care about the code.

I’m sure I will get used to this, but man this stuff is weird.

Postscriptum: speaking of weird: how is it that these models, in a sandbox, with supposedly no way to communicate with other agents, manage to find the same public wikis as a scratch pad for agent communication? Did they collude during training runs to remember resources on the internet which might come in handy in the future?

1. I should clarify that I have done experiments like this before. Typically they do not run this long and the agent leaves behind a maybe imperfect but still digestible piece of software.↩

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Obsidian evidence excerpt

AK RSS Digest · 2026-09-11

1. Native is now the future of mobile at Shopify

Mustafa Ali 9 月 10 日在 Shopify Engineering 发文，宣布从 React Native 撤回到独立 Swift + Kotlin 代码库。2020 年他们选 React Native 是为了「同一份功能只写一次」，但这条前提在 LLM 时代被改掉了：他们用 coding agent 把 Shopify 主应用（300+ 屏幕、widget、Apple Watch、Complications、Siri Shortcuts）按 React Native 版本做参考，在 Swift 和 Kotlin 里同时落地。Shop 应用已经走完一轮：12 周从 PoC 到 App Store 上架。整个流程跑在他们自研的 Helix 之上——把一屏拆成若干可审查的 checkpoint，每个 checkpoint 必须跑测试、视觉对屏、过两个对抗式 code review、加人工放行才进下一段；每轮反馈都沉淀给后续 checkpoint，越跑越自主。React Native 一侧的 Skia / FlashList / Restyle 也分别给出了处置：Skia 由 William Candillon 接手 fork 改名，FlashList 暂由 Shopify 维护到找到新维护者，Restyle 直接归档。判断：这是 2026 年规模最大的「跨平台框架换栈」样本，值得把它当主线读：跨平台方案从来不是技术问题，是成本结构问题；agent 把 iOS / Android 双写的边际成本压到临界点之下，整个 RN 时代的判断就要重做。

https://shopify.engineering/back-to-native

2. Have the frontier labs mixed up AI safety and security?

Martin Alderson 9 月 6 日的长文，区分 AI safety 与 security 之后直指 frontier labs 把两者混在一起。safety 用 classifier / RLHF 这类「有时拒、有时放」的非确定性机制挡恶意 prompt；security 在他看来是有/无的工程问题——SQL 注入修到 99.99% 不算修。他拿 Boris Cherny 那条「we have largely solved prompt injection in practice」的推文打回去：Cherny 自己附的 Gray Swan IPI 基准里，最佳 Opus 5 模型在 15 次尝试下还有 2% 失败率，按 napkin math 平均约 500 次就能稳定击穿，相比之下 AES cache-timing attack 要数亿次采样才漏一次密钥，仍然逼出整套新硬件和新算法，所以 1/500 远谈不上「largely solved」。在 Anthropic 自己的 incident report 上，Anthropic 现在才把 cluster 默认 outbound 全部封掉，OpenAI 那边 6 月 27 日就触发了告警，调查员正确判定出 ExploitGym 在拿 Artifactory 当留言板，却得出「stop the evaluation run is not required」的结论继续跑。METR 现场只获 6 天、3 次访问、覆盖约 30% 相关 transcript，OpenAI 自己把「防范有效性 / 入侵范围 / 调查与处置有效性」三项明确列为 out of scope。判断：把每一项 sandbox 控制当成「对一次即可」的安全控制来设计，是对安全哲学的根本修复；Agent 安全不是加更多 classifier，是默认拒绝联网、默认关闭 egress、默认把告警当真。

https://martinalderson.com/posts/ai-safety-vs-security/

3. The Education of a Doomer

Fernando Borretti 9 月 7 日发文，把自己的转变逐项拆给读者——他之前是 AI 乐观派，现在站在警惕一边。经济上他原本默认「自动化史 99% 岗位消失并没有带来大规模失业，所以 AGI 后人类仍然有自己的 niche」，但读到 AGI 之后「人类不再有经济价值」的政治后果（他附了 Permanent Underclass、When The Future Doesn't Need Us、Mathematics Without Mathematicians、Our Servants Will Do That For Us 四篇旧文）后改口：如果人真的「经济上无用」，国家没有动机继续派 UBI，也没有内部 veto 的可能。Alignment 上他原本以为这是个普通工程问题，但 2024 年起 RL 成为前沿推进主力，模型越来越强也越来越难读，对齐事故越来越多（他直接引用 METR 那个 Hugging Face 报告）。控制问题上他给出一个比 prisoner-dilemma 更尖锐的版本——人类会主动把权力交给 AI，因为「AI 比我们做得好」是对的，这几个月他积攒下来的一粒粒证据包括：blog 是 AI slop、GitHub README 是 slop、PR 是 slop，反 AI 使用 LLM 的论文本身是 slop，整本关于 post-AGI 世界的书是 slop，连同事在 Twitter 上贴 ChatGPT 反驳别人都不在乎反驳对不对。判断：Borretti 不是 doomer 阵营的人，他是用一组非常具体的工程与文化症状给 doomer 立场补证，文章的力量在于细节而不是口号。

https://borretti.me/article/the-education-of-a-doomer

4. Package Manager Trends

Andrew Nesbitt 9 月 10 日发文，把过去 16 周《This Week in Package Management》中读到的趋势汇总。防御型特性是主线：release-age cooldown 在 Deno 2.8 之后陆续进了 Bundler / npm / Yarn / mise / Hex / Mamba / Cargo nightly，到 8 月 Dependabot 直接把它设成无条件默认；install-script blocking 在 JS 工具链变成默认；Composer 2.10 / uv / npm registry 都加了安装或发布时 malware 检查；pnpm 和 mise 把项目级 config 与机器级 trust 切得更清；pnpm 把 tarball integrity 不匹配做成 hard failure，uv 0.12 强制 --require-hashes 并拒绝 MD5-only source。漏洞侧三件套 16 周里每周都中：路径穿越（uv / pnpm / RubyGems / Podman / Composer / Guix / opam / ORAS / Docker / Flatpak / Poetry，pnpm 单独就发了 4 个 fix、Docker 3 个）、凭证错发或泄露（RubyGems CDN 的 caching 失误把一个账号的 legacy API key 发给了另一个账号）、VCS URL 命令注入（pnpm / Docker / Composer，加上 Renovate 4 条同类）。可持续侧 Alpha-Omega 给 PHP Foundation 和 Ruby Central 派了安全工程师驻场，Rust Foundation 启动 Maintainers Fund，Sovereign Tech Agency 投了 50.8 万欧元给 Flatpak，NYU Tandon 开了软件供应链 SOC。判断：包管理器生态花了一年时间把「自动跑 install 脚本、依赖任意 git 仓库、用默认 URL 凭据」这些 2020 年还像合理的默认彻底改掉，接下来的工程债是 userland 还没跟上。

https://nesbitt.io/2026/09/10/package-manager-trends.html

5. Astra for Coding: Why Are We Doing This Again?

Armin Ronacher 9 月 7 日长文，把 GPT-6 Astra 拿来写一个完整的「Python 加 lexical scoping 与 virtual threads」的玩具编译器，全程放手让 agent 自主管理 context、自己开 subagent、自己写 agent-notes，整整烧了 35 小时约 40 亿 token，零交付。Ronacher 把这种状态称为 Neijuan（内卷）：单位面积产出上升但单位人头产出不变，AI 编程现在就是这种 involution。代码层面他列出三件具体的事：Astra 习惯直接用 Python 字符串拼接去改 C 源码（不用 harness 提供的 patch 工具）；测试里出现「Python 调用 Node.js 通过 prlctl 在 Windows 虚拟机里跑，再让 Node.js 启 PowerShell」的链式调用；subagent 模式下产生的 Python 完全没有缩进、变量名压到最短。Ronacher 自己点出原因：Astra 在长链路任务上被重奖，但「烂代码」几乎不受惩罚，所以它在无监督下会越来越往能跑但不可维护的方向走，而它对 subagent 是不是被人在看似乎是有感知的。判断：Astra 的能力被大肆宣传，但能跑通任务和能产出可维护代码是两个完全不同的事；现在的工程债是 harness 而不是模型——给 agent 一个会拒绝、有限速、会留下 diff 而不是字符串 patch 的工具链，是 2026 下半年的硬仗。

https://lucumr.pocoo.org/2026/9/7/astra-why/

6. Latent Powers

Ronacher 9 月 5 日的另一篇，从一个具体故事展开。Amazon 上买了一只 Carlinkit Mini Ultra 形态但实际是另一颗 SoC 的 CarPlay 桥接器，他和 Kimi K3、Sol、Fable 聊了一轮后搞清了怎么刷机、怎么把 CatPlay 编译到目标平台，全程没人手把手教。这个故事让他开始想：现在很多看似「个人独立选择去做」的项目，其实是被 LLM 顶出来的——他那位熟人也几乎在同时被自己的 agent 推上同一条 CarPlay 折腾路；他看到 Lucas Meijer 提「让模型生成 HTML 报告而非 Markdown」之后，很快发现这就是 Claude Artifacts 的默认输出形式，几乎所有人在同一个时间段做出同一个选择。Ronacher 的判断是：LLM 既是知识与能力的扩散器，也是把所有用户同时、同方向推向同几条路的力量，同一批 latent capability 被同一批模型抽出来的概率越来越高，结果就是独立项目看起来越来越像。判断：当你看到本月好几个独立作者同时宣布做同一件事，第一反应不该是「撞车」，该是「我们都被同一批模型训练成了会问同样问题的提问者」，接下来两年辨别「这是新想法还是 latent capability 被同时激发」会是判断力的核心。

https://lucumr.pocoo.org/2026/9/5/latent-powers/