DEV Community: A. F. Sadek

Is Claude actually getting worse, or are we looking in the wrong place? Between Stella Laurenzo’s deep-dive data from AMD, Anthropic’s postmortem admissions, and growing regulatory pressures, 2026 has made one reality unavoidable: the gap between benchma

A. F. Sadek — Mon, 24 Aug 2026 09:30:45 +0000

A. F. Sadek

Aug 23

Is Claude Getting Dumber? You May Be Looking at the Wrong Part

#ai #programming #productivity #anthropic

7 min read

Is Claude Getting Dumber? You May Be Looking at the Wrong Part

A. F. Sadek — Sun, 23 Aug 2026 16:45:26 +0000

Prefer reading in Arabic? Read the Arabic version.

The question began with real frustration: Is Claude getting dumber?

That is the phrase people reach for when the product changes its habits. You ask it to inspect a project, and it edits before reading. You give it a long task, and it stops halfway through. You ask for a number, and it answers with confidence before doing the measurement.

Claude has no bad mood. The experience still arrives in human terms: laziness, hesitation, verbosity, forgetfulness, and confidence that outruns the evidence.

“Dumber” compresses all of those problems into one word. It also hides the more useful question: Which part of the system changed?

Users measured a real change

Throughout 2026, developers reported similar problems on GitHub, Reddit, and X. The details varied: shallow reasoning, premature stopping, lost context, long explanations with little action, odd tool choices, and confident conclusions made before verification. By April, the pattern had drawn coverage from Axios and The Register.

Stella Laurenzo, a senior AI director at AMD, filed one of the most detailed reports. Her April 2 GitHub issue analyzed 6,852 Claude Code sessions, including 17,871 visible thinking blocks and 234,760 tool calls. The work included systems programming, GPU drivers, MLIR, and long tasks spanning many files.

The numbers were striking:

Estimated median visible reasoning fell from about 2,200 characters in late January to about 600 by mid-March.
The ratio of file reads to edits fell from 6.6 to 2.0.
Edits made without a recent file read rose from 6.2 percent to 33.7 percent.
A custom hook for premature stopping and responsibility-dodging fired 173 times after March 8 and zero times before it.

This dataset came from one user’s environment during a period when workloads and product versions changed. Some reasoning depth was also estimated through proxy signals after Anthropic redacted thinking blocks. The analysis gives strong evidence of a behavioral change in that system. It leaves the scale of the problem across the wider user base open.

Anthropic then published the evidence that settled the central part of the argument.

What Anthropic changed

On April 23, the company released a technical postmortem on the quality reports. It found three separate causes:

Lower reasoning effort. On March 4, Claude Code changed its default effort from high to medium to reduce latency and token use. Users received faster responses with less intelligence. Anthropic reversed the decision on April 7.
A context-management bug. On March 26, Anthropic shipped an optimization for sessions resumed after a long idle period. The feature was meant to clear old reasoning once. A bug kept clearing it on every later turn. Claude became repetitive, forgot why it had made earlier choices, and used tools in ways that seemed inexplicable. The bug was fixed on April 10.
A brevity instruction that hurt quality. On April 16, Anthropic added a system-prompt instruction limiting text between tool calls to 25 words and final answers to 100 words unless the task required more detail. Wider testing later found a 3 percent drop in coding quality for Opus 4.6 and 4.7. Anthropic removed the instruction on April 20.

Anthropic said its API and model-serving system were unaffected.

The central lesson is clear: the core model can stay the same while the delivered experience becomes measurably weaker.

You use a whole service

When you select “Claude Opus,” you receive a full system:

The model.
The system prompt.
The reasoning-effort setting.
The policy for keeping and pruning context.
Tool descriptions and permissions.
The cache.
Safety classifiers.
Fallback routes to other models.
Usage limits.
Cost and latency decisions.

Every layer can change the result. A smaller reasoning budget pushes the model to act sooner. Bad context pruning makes it repeat completed work. A harsh brevity instruction removes steps it needs to finish correctly. A broad safety classifier blocks a sound request before the work starts.

The model name identifies the engine. The service sets its operating conditions.

This is why benchmark arguments often miss the complaint. A benchmark measures a named model inside a specified harness. A user experiences a changing service under real constraints involving cost, speed, context, and safety.

Opus 5 can score higher than its predecessor and still perform worse on a particular task because the product gave it less effort or removed important context. Those statements describe different layers of the system.

Opus 5 makes the gap visible

Anthropic introduced Claude Opus 5 as a leading model on coding and knowledge-work evaluations, with stronger verification, initiative, and efficiency than its predecessor. The launch material also described effort controls, safety classifiers, and automatic fallbacks to Opus 4.8 when classifiers flag a request.

Those details matter. The name shown in the model picker tells only part of the story. A request can begin with Opus 5 and then meet a safety classifier or a fallback route. The same model can also run with a different reasoning budget from one session to another.

Complaints continued after launch:

An August GitHub report documented two long sessions that kept using tools for hours while producing almost no visible narration. The work continued, while the human supervisor had little idea what the system was doing.
Another user described confident recommendations made before measurement, followed by reversals after the user had already accepted the advice.
A third report recorded a benign mechanical-engineering request blocked by Opus 5 safeguards eight times over roughly two hours.

These are individual issue reports. Their value comes from checkable details; their limit is the lack of a population-wide measurement. Anthropic’s April postmortem gives us a reason to treat them seriously: the company’s internal evaluations initially failed to reproduce problems that users were already seeing.

Where politics enters the picture

Another question surrounds this story: Did Claude become more cautious because Anthropic’s relationship with the US government grew more tense?

In February 2026, negotiations with the US Department of War reached a crisis over two limits Anthropic insisted on: no use of its models for mass domestic surveillance, and no use in fully autonomous weapons. On March 4, the department designated Anthropic a supply-chain risk. The company challenged the decision. In his March 5 statement, Dario Amodei also said Anthropic would continue supporting work including intelligence analysis, modeling and simulation, operational planning, and cyber operations.

Then came the strike on the Shajareh Tayyebeh elementary school in Minab during the Iran war. More than 100 children were killed. The Associated Press reconstructed evidence pointing to a US strike while the Pentagon continued to withhold the findings of its investigation from the public.

No public evidence establishes that Claude selected the target, that a Claude output caused the strike, or that Anthropic changed Claude’s everyday coding behavior in response. This point deserves a direct negative statement. Turning timing into causation would repeat the failure users are criticizing: a confident conclusion before verification.

Government influence over frontier-model deployment widened in June. Anthropic announced a US directive suspending foreign-national access to Fable 5 and Mythos 5. The administration also asked OpenAI to begin the release of GPT-5.6 with a small group of approved partners, according to Axios.

The political context explains the environment in which companies design safety layers, routing rules, and access controls. The April degradation has documented product causes: an effort setting, a context bug, and a system-prompt instruction.

We should ask how politics shapes the product. A sound answer needs evidence connecting a political decision to a specific technical change.

A better diagnosis: deployed intelligence

The useful question is: How much of the model’s capability reaches the user, and under what conditions?

The model may be capable of reading a codebase deeply while the service gives it a smaller budget. It may reason well and then lose part of that reasoning through context management. It may understand the request and then meet an external classifier or a fallback to another model.

These are changes in deployed intelligence: the intelligence that reaches the user after passing through the product stack.

Anthropic’s changelog should treat the following as changes to effective product quality:

System-prompt edits.
Default reasoning effort.
Context retention and pruning.
Safety classifiers.
Fallback routes.
Tradeoffs among cost, latency, and reasoning depth.

The company should also evaluate the exact public build and show users which model answered, why a safety system intervened, and whether a fallback route was used.

Governments owe the public similar transparency about the role these systems play inside operational decision chains. “A human remains responsible” identifies the accountable person. An audit trail shows how the decision reached them.

So, is Claude getting dumber?

The short answer: Claude’s delivered experience became weaker during specific periods, and we now have direct evidence that the product layer caused an important part of that decline.

The model name alone cannot explain what users see. Everyday Claude is the product of capability, budget, context, prompting, safety, and interface design. A failure in one factor lowers the whole result.

The intelligence being sold is increasingly conditional on settings the user cannot inspect: a hidden prompt, a reasoning budget, a cache state, a classifier, a routing decision, and a fast-changing political and regulatory environment.

A benchmark score is visible on a chart. The conditions shaping intelligence should be visible too.

Sources and further reading

Anthropic, “An update on recent Claude Code quality reports”, April 23, 2026.
Anthropic, “Introducing Claude Opus 5”, July 24, 2026.
Stella Laurenzo, Claude Code issue #42796, April 2, 2026.
Axios, “Anthropic’s AI downgrade stings power users”, April 16, 2026.
The Register, “Claude is getting worse, according to Claude”, April 13, 2026.
Anthropic, “Where things stand with the Department of War”, March 5, 2026.
Associated Press, reconstruction of the Minab school strike, July 1, 2026.
Anthropic, statement on access restrictions for Fable 5 and Mythos 5, June 12, 2026.
Axios, “Trump administration asks OpenAI to limit next model release”, June 25, 2026.

DOXASCOPE: In The Next Place; The Mist Does Not Judge.

A. F. Sadek — Sat, 18 Apr 2026 18:38:48 +0000

DOXASCOPE

المكان التالي | The Next Place

SPOILERS AHEAD!

I am building DOXASCOPE, a bilingual transmedia universe.

It begins as a novel. It expands through an interactive website, scene films, audio, visual fragments, and eventually a game.
But the core is not the format.

The core is a question:

What if something of you remained after death?

Not your memory as a clean archive.

Something stranger: the pattern of you.

Your beliefs. Your contradictions. Your unfinished logic. Your private assumptions about what is real.

In The Next Place, death does not place you in a moral courtroom. It does not simply reward, punish, or explain.

It places you in the Mist.

And the Mist does not judge what you believe.

It responds to how coherent you are.

Like physics.

Not like morality.

The Mist

The Mist is not only a setting. It is the operating environment of the story.

In ordinary fiction, a character can believe one thing, say another, and do a third. That can create drama.

In the Mist, that contradiction has weight.

Identity is a high-resolution file. In the Mist, most are just thumbnails.

Some people become clearer. Some fracture. Some drift. Some cling to systems. Some join structures larger than themselves. Some try to measure the impossible.

The Mist does not answer every question.

It asks better ones.

Four Ways to Survive

The world forms around four major paths.

Each one is a way of surviving truth.

The Fortress

Order through doctrine.

The Fortress believes reality must be held together by structure, obedience, and inherited certainty.

The Drift

Freedom through solitude.

The Drift resists systems. It trusts experience, motion, and the refusal to be pinned down.

The Weave

Strength through unity.

The Weave treats identity as something shared, merged, and carried through collective meaning.

The Network

Truth through measurement.

The Network approaches the Mist like a hostile unknown system: observe, test, map, repeat.

None of these paths is simply correct.

Each one reveals something.

Each one costs something.

Three Doors

At the edge of the journey are three outcomes:

Transcendence.

Transformation.

Annihilation.

These are not just endings. They are consequences.

The story is interested in what kind of person, system, or civilization reaches each door, and what they had to become to get there.

Six Eras

The universe is structured across six eras.

Era Zero begins before time.

The First Era brings matter, stars, and the physical universe.

The Second Era brings life, consciousness, and civilization.

The Third Era is where the Mist begins to take shape.

The Fourth Era is Adam's journey through the novel.

The Fifth and Sixth Eras move into the game and the wider leak of the Mist into other layers of reality.

I do not want the world to feel like lore attached to a story.

I want the story to feel like a visible slice of a larger system.

One Universe, Several Forms

DOXASCOPE is being built through several forms because each one can ask the central question differently.

The novel follows Adam as he wakes in the Mist without memory and tries to understand where he is, what he was, and what the doors mean.

The website is a revelation engine. It does not dump the world at once. It unlocks pieces in controlled layers: novel text, audio, visual fragments, leaks, and eventually wiki reveals.

The scene films turn selected moments into bilingual cinematic fragments.

The game will not be a simple adaptation. It will be another instrument for the same question: how do your beliefs behave when a world starts measuring them?

Why Build It This Way?

Because the idea only works if the form obeys the same law as the world.

The novel is about coherence.

So the project has to be coherent too.

The website cannot reveal everything too early.

The videos cannot explain everything.

The wiki cannot behave like a normal encyclopedia.

The game cannot simply reward "good" answers.

The whole structure has to move like the Mist itself: partial, responsive, layered, and slightly dangerous.

That is the thing I am trying to build.

Not just a story with a website attached.

A story whose architecture is part of the story.

Start Here

Read the first public chapter:

DOXASCOPE — The Next Place | المكان التالي — Enter The Mist

ماذا يبقى منك بعد الرحيل؟ تجربة كشف تدريجي من عالم رواية المكان التالي. The Mist does not judge. It only asks.

doxascope.com

Watch the first scene film:

From the first electron to the last thought:

one story.

The Playbook - To Build A World

A. F. Sadek — Tue, 14 Apr 2026 08:46:16 +0000

I couldn't find the tools I needed, so I built them. Then I built the thing.

Phase 0 - In the beginning, there was no beginning...

This started as scattered notes. Ideas on paper, loose questions, fragments of a world that wouldn't leave me alone. That was about twenty years ago.
I do software engineering by trade. Arabic speaker. I've worked in project management, in tech, in systems. And for two decades, alongside everything else, I've been building something I couldn't explain to anyone in a single conversation.
The short version: a fictional universe that spans a novel, a web game, a website, and the early blueprints for a TV series and comic. All connected. All independent. All built from the same philosophical core.
The long version is the one I want to tell. Not about the universe itself (that has to wait; it's full of spoilers I'll take to my grave before revealing early), but about what it takes to actually build something like this alone.

Start with an axiom. One idea that is true at every scale of your world, from particle physics to character psychology to political structure to game mechanics. Not a theme. A law. If your axiom doesn't constrain you, it's not an axiom. If it doesn't generate consequences you didn't plan, it's not deep enough.

The Novel Problem

It started as a novel. Bilingual, Arabic is the source language, English follows as a parallel draft, not a translation. Two different books wearing the same plot.
The first problem was finding a writing tool that supports Arabic properly. RTL layout, proper typography, the ability to track characters and locations and world rules while writing. I tried everything. Markdown editors. Scrivener-style tools. Novel-writing software. Added RTL to Manuskript. None of them handled Arabic without fighting me.
So, I built this because I needed it. Nobody else was going to.

Write the world bible before you write the story. Derive the factions, the physics, the geography, the economy, and the power structures from the axiom, not from plot convenience. When two systems contradict each other, the axiom wins. When the axiom produces something uncomfortable, keep it.
Write the story in the language it thinks in. Translate later as a parallel work, not a copy. The source language carries things the target language cannot, and that asymmetry is a feature.

The Game Problem - The Framework

At some point, the novel's philosophical framework outgrew prose. The ideas needed to be experienced, not just read. So I started designing a web game.
The game needed its own design document, its own mechanics, its own validation system. I extracted the philosophical skeleton from the novel, the questions it asks about belief, truth, and consistency, and distilled them into interactive systems. Fifteen philosophical conflicts. Four modes of interpreting reality. An endgame that doesn't tell you what's "correct", it shows you what you chose, and what that reveals.
To validate that the system worked before putting it in front of real players, I built a separate tool: a historical profiling simulator. You feed it documented decisions made by real historical figures — Napoleon, Galileo, Curie — and the engine generates a psychological profile. Then you compare the output against what scholars actually say about that person.

The demo is complete. Not deployed yet.

Extract the interactive framework from the fiction, not the other way around. The game, the experience, the simulation — they are lenses on the same axiom, not adaptations of the story. They must stand alone and connect to everything.
Every tool you build becomes infrastructure for the next layer. The tool is not a detour; it is the road.

The Media Problem

The game needed assets. Images, audio, video. I'm one person. I don't have a studio. What I do have is patience and a willingness to test every AI-assisted media tool that exists. I spent months — I've lost count of how many — evaluating and building personal production pipelines. Image generation. Video composition. Audio synthesis. Voice generation with quality control.

I ended up with a set of workflows that let me produce above-average media assets across every format I need. Not perfect. Above average. Consistently. Solo.

For the social media presence, I built an automated reels pipeline. It takes a text beat, generates audio via edge TTS, runs Whisper transcription for quality assurance (I require a 1.000 QA score — perfect transcript match — before any reel goes public), generates timed karaoke subtitles, and composes the final video. Arabic and English variants. Each reel under 59 seconds. I've even recorded the first batch with my own voice.

The pipeline is a Python script with a PowerShell launcher, running locally.

Build production pipelines for every medium you need — image, audio, video, text — and accept "above average, consistently" as the quality target. Perfection is for teams. Consistency is for solo operators.

The Website Problem

I needed a place to put all of this. Not a blog. Not a portfolio. A revelation engine. A website that controls exactly how much of the universe is visible at any moment, with information that can only be unlocked forward, never backward.

So I built that too.
339 tests passing. ~38,600 lines of code across about 250 files.
One developer.

DOXASCOPE — The Next Place | المكان التالي — Enter The Mist

ماذا يبقى منك بعد الرحيل؟ تجربة كشف تدريجي من عالم رواية المكان التالي. The Mist does not judge. It only asks.

doxascope.com

Deploy through a revelation architecture — a system that controls how much of the world is visible at any moment, releases information monotonically, and treats the audience's discovery as a designed experience, not a marketing event.

The Coordination Problem

My workspace currently tracks 113 projects. Most are idle or frozen, experiments, tools, personal projects, and archives from the media pipeline journey. About 14 are actively maintained. The main creative container and its sub-projects are the core.

I coordinate across multiple AI agents: different models for different tasks, routed by complexity and capability. There's a shared context bridge that stays current, a PM layer with automated GitHub sweeps across repositories, and a handoff system so any agent session can pick up where the last one left off without losing state.

I built a memory filesystem for cold-start recovery. I built a shadow export system for feeding project context into retrieval-augmented notebooks. I built commit tracking scripts, backup pipelines, and a coordination document that any new session — human or AI — reads first to understand the current state of everything.

Manage the entire operation as a workspace, not a project. Track everything. Automate what recurs. Route work to the best available tool or agent/or team if you have, by task, not by loyalty. Document for your future self; the most important collaborator you will ever have.

The Patience Problem

Some might see it as distraction. A dead end where projects go to collapse. But I am patient. I have been building for years.
I'm building because I have something heavy to lay down. Not to prove anything. Not to disprove anything. This universe asks a question that I think deserves a world built around it. I can't say what the question is without spoiling everything.

But I can say this: every tool I built, every pipeline, every automation, every agent coordination system. They all exist to serve one thing. A story written in Arabic first, because that's the language it thinks in. Published bilingually, because the story breathes differently in English. Experienced through a website that controls its own revelation. Playable through a game that asks you the same questions the novel asks its characters.

If the work lands, or if it doesn't, I built something real. The tools exist. The pipelines work. The code is tested. The words are written.

And somewhere in that stack of projects and 20 years of fragments, there's a universe that I think has something to say.
It'll say it when it's ready.

And know this: no one will care for a long time. Build anyway. The axiom holds or it doesn't. The audience arrives or it doesn't. The work is the work.