Productivity

Liam Powell used Bend 2 to show a common vibe-coding trap, where AI builds a whole language for a job that SPARK already solves in a few lines

September 18, 2026 at 4:31 PM PT

Reveneau signal-field composition for a post on vibe coding

Why it mattersA team using AI to build a substantial system needs one hour of research before it opens the editor. Without it, an LLM will produce a working answer to a question a five-minute survey of the field would have replaced with a shorter one.

Liam Powell published a critique of Bend 2 on 18 September titled "Bend 2 and the Vibe-Coding Trap." It reached 308 points on Hacker News, and its argument is that Bend's demo is an entire language reinventing a category of software, formal verification, whose current standard tool would have solved the same demo in a fraction of the code. Powell treats Bend as an example, not the target: the same trap catches any team that builds a substantial system before learning the field it sits in.

Bend 2 asks a developer to write "laws" and lets an LLM write proofs the compiler then checks. The demo on Bend's homepage states that the player can never touch the flag or win the game. Powell counted 58 lines in the LAWS.bend file that state the property, and 442 lines in the accompanying PROOF.bend file that the model produced to prove it.

The comparison

Powell vibe-coded the same demo in SPARK, an open-source language and compiler for formal verification, with no guidance beyond "recreate this demo." The resulting Ada program declares a state, a wall function, a Safe ghost function, and a Step procedure with a postcondition. Running GNATprove returns "Success: all checks proved (12 checks)." The complete file is short enough to read on one screen.

Powell's point is about the field itself. Bend's author appears to have built an entire language around formal verification without noticing that formal verification is an established field with mature tools. The phrase "formal verification" appears nowhere on Bend's site or in its codebase.

The class of failure

Powell's title names the failure: vibe coding lets a developer produce a substantial solution before learning enough to recognise a better one exists. When a person asks an LLM for a system that proves function correctness from first principles, the model will build it. The model will not stop and say the field already has tools that do this without the model, and that the person is asking for the wrong thing.

The Bend author later commented on the Hacker News thread, and Powell added a note pointing to that comment. Powell also says he does not know the author's history with language design and that his real objection is the vibe-coding pattern, not the person.

What this changes for a team

The reader-facing consequence is a change in the order of work when using AI to build a new system. One hour reading the field, listing existing solutions, and choosing which of them the new system is meant to beat comes before opening the editor. Code produced without that step still runs, so the failure is silent: the model builds a 500-line answer where 30 lines would have done, and nobody sees the shorter version because it was never asked for.

Bend 2 launched earlier this month with a claim that AI writes the implementations and the compiler checks the proofs. The launch story is a real one, and it ran on this AI News on 17 September. Powell's critique is a separate story: it is not about Bend but about the pattern that catches every team building with a model.

Source

Reported by: Liam Powell's Blog

This item was written by an AI system from the linked source. Reveneau is responsible for what it publishes.

More from AI News

An engineer argues LLMs should be treated as feature extractors for a logistic regression, beats the competition winner on an irony dataset with 0.747 F1

A working data scientist argues teams should stop using an LLM as a classifier and start using it as a feature extractor for a plain logistic regression, and shows the pattern reaches 0.747 F1 on the SemEval-2018 irony dataset, above the competition winner's 0.705.

Source: Hacker NewsProductivity

Calif Research says AI found a zero-click WeChat bug in two days, and its team turned it into a cross-platform worm demo in three weeks

Security firm Calif Research says AI found a memory corruption bug in WeChat's VoIP stack in about two days, and its team then built a cross-platform zero-click worm demo in three weeks. Tencent patched the flaw before publication.

Source: PressProductivity

Nitin Garg's study of AI-generated code review reports the same bug-catch rate with or without a written spec, and 81 percent finding-to-requirement attribution with the spec against 0 without

Nitin Garg's within-subject study of five reviewers on two AI-generated banking services measured 81 percent finding-to-requirement attribution with a written specification against 0 percent without it, at a cost of 21 minutes per review, with no change in the recall rate.

Source: PressProductivity