Forem: Akshat Uniyal

GPT-5.5: OpenAI’s Smartest Model Yet — But Is the Hype Bigger Than the Model?

Akshat Uniyal — Wed, 29 Apr 2026 16:11:47 +0000

Every few weeks, the AI world picks a new model to argue about. Last week was OpenAI’s turn. GPT-5.5 landed on April 23rd — less than two months after GPT-5.4 — and the reaction followed the usual pattern: breathless praise from some corners, eye-rolls from others, and a wall of hot takes from people who hadn’t actually used it yet.

Some called it a leap. Others called it a glorified patch. I’ve spent the past week reading the benchmarks, the developer breakdowns, and the early enterprise reports to figure out which is closer to true. The answer is neither — and that’s exactly what makes it worth understanding properly.

What OpenAI Actually Built

GPT-5.5 is not a better chatbot. That distinction matters more than it sounds.

This is a model designed to do work — autonomously, over long stretches, with minimal hand-holding. You throw it a messy, multi-part task and trust it to plan, use tools, course-correct, and keep going without constant prompting. Previous models needed careful shepherding. GPT-5.5 is designed to carry more of the weight itself.

In agentic and terminal-based workflows, it earns that claim. On Terminal-Bench 2.0 — a benchmark that tests real command-line tasks involving planning and tool coordination — it scores 82.7%, the highest of any publicly available model right now. It also more than doubles its predecessor’s long-context recall at one million tokens, jumping from 36.6% to 74.0%. These aren’t headline-padding numbers; they translate into real performance on real tasks.

It also completes the same work with fewer tokens. OpenAI argues this makes the effective cost increase roughly 20% over GPT-5.4, despite nominally doubling the API price. Whether that math holds for your specific workload is worth testing — but the efficiency direction is genuine.

Enterprise signals are encouraging too. The Bank of New York, which had early access, reported a “step change” in accuracy and hallucination resistance. Banks don’t reach for superlatives lightly.

Where the Hype Outruns the Reality

OpenAI’s launch framing positions GPT-5.5 as a decisive step forward. On certain benchmarks — the ones OpenAI chose to headline — that’s true. But look at the full picture and a more complicated story emerges.

Out of ten benchmarks that both OpenAI and Anthropic report on, Claude Opus 4.7 — Anthropic’s flagship, released just one week before GPT-5.5 — leads on six. The categories where it wins aren’t peripheral: GPQA Diamond (graduate-level science reasoning), SWE-Bench Pro (real-world GitHub issue resolution), and MCP Atlas (tool orchestration). On SWE-Bench Pro, the gap is 64.3% for Claude versus 58.6% for GPT-5.5. That’s a meaningful margin in any production engineering context.

Tom’s Guide ran both models through seven difficult real-world tests covering logic, reasoning, and domain knowledge. Claude won all seven. The most revealing moment: given an impossible logic puzzle, GPT-5.5 confidently hallucinated two solutions that violated the problem’s own constraints. Claude flagged the puzzle as impossible. That difference — honesty versus false confidence — is exactly what doesn’t show up in a marketing benchmark table, and exactly what gets you into trouble in high-stakes contexts.

The hallucination problem hasn’t been solved. GPT-5.5 has improved, but it still tends to reach for an answer rather than admit uncertainty. For casual use, that’s tolerable. For anything in a regulated industry, a medical workflow, or a legal context — where a confidently wrong answer is materially worse than a careful “I’m not sure” — it remains a genuine liability.

On pricing: the API sits at $5 per million input tokens and $30 per million output — double GPT-5.4’s rate. At 100 million output tokens a month, that’s $3,000 for GPT-5.5 versus $2,500 for Claude Opus 4.7. OpenAI’s efficiency argument may close that gap for token-heavy pipelines. It won’t close it for everyone.

The Honest Competitive Picture

Right now, the AI frontier isn’t one model ruling everything. It’s two strong models that optimized for different axes — and the gap between them depends entirely on what you’re actually building.

GPT-5.5 is the clearer choice for terminal-first, shell-driven, DevOps-style agent workflows. It’s faster in interactive sessions, more token-efficient at scale, and deeply integrated with OpenAI’s Codex ecosystem. If you want a model that drives a loop end-to-end without pausing to explain its reasoning, this is your model.

Claude Opus 4.7 is the better fit for complex, reasoning-heavy software engineering — large codebase reviews, multi-language refactoring, or any output where a human is going to scrutinize the result carefully. It’s more verbose, which can feel sluggish in quick back-and-forth sessions, but in high-stakes contexts that deliberateness is a feature, not a bug.

The April 2026 AI frontier is, in a meaningful sense, a two-model world. The most effective teams aren’t picking one and defending the choice — they’re routing tasks intelligently between both.

Three Things Worth More Attention Than They’re Getting

The release cadence is itself the story. GPT-5.5 arrived six weeks after GPT-5.4. OpenAI is now running on a sub-two-month cycle for frontier models. That pace changes the calculus for teams making model commitments. If you integrate tightly into any single model’s API today, you’re betting on a snapshot that may be obsolete by summer. Model-agnostic architecture isn’t a philosophy anymore — it’s a risk management decision.

Open-source is closing the gap faster than the headlines suggest. DeepSeek V4-Pro scores 80.6% on SWE-Bench Verified and 67.9% on Terminal-Bench 2.0, at $3.48 per million output tokens — roughly one-ninth the cost of GPT-5.5 Pro. For high-volume pipelines where the workload fits, the proprietary advantage is genuinely thin. This isn’t a distant future threat. It’s April 2026.

The cybersecurity dimension is about to become impossible to ignore. OpenAI delayed API access for GPT-5.5 specifically because it required different safeguards around cybersecurity risk. That’s not boilerplate legal caution — it reflects how capable these models are becoming at identifying software vulnerabilities. Anthropic restricted its own Mythos model for the same reason. The industry is arriving at a moment where the most capable AI tools are also the most dual-use, and the regulatory and security conversation around that is still catching up.

The Bottom Line

GPT-5.5 is a genuinely good model — fast, efficient, and for the right workloads, the best option currently available. But “best for some things” is not the same as “worth the hype,” and the launch-week coverage has done a poor job of drawing that line.

It costs more than its closest competitor. It trails Claude on reasoning depth and precision tasks. It still hallucinates with confidence in ways that matter for serious use cases. None of these are disqualifying — but they’re real, and anyone making a decision based purely on OpenAI’s benchmark table is making a half-informed one.

Here’s the thing about tools this powerful: the question was never “which model is smartest?” The question is which model makes you more effective, on your tasks, within your risk tolerance. That question has a different answer for every team.

And that answer only comes from testing. Not from benchmarks, not from launch-day press briefings — and not from blog posts, including this one.

Used GPT-5.5 in your own workflow yet? I’d love to hear what you’re finding — the real-world signal is always more interesting than the launch-day noise.

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

Who’s Accountable When AI Gets It Wrong?

Akshat Uniyal — Mon, 27 Apr 2026 12:07:11 +0000

Originally published at https://blog.akshatuniyal.com.

A plain-language guide to Responsible AI — and why it matters more than most people realise

Picture a loan application. A person applies, gets rejected, and asks why. The bank says the model decided. The model’s vendor says it just built the tool — how the bank configured it is on them. The bank’s data team says the training data came from a third party. The third party says they only supplied the data, not the logic.

Everyone touched the system. Nobody owns the outcome.

This is not a hypothetical. It’s Tuesday.

The word nobody can agree on

“Responsible AI” has been on enough conference slides and annual reports that it’s started to sound like wallpaper. Which is a problem — because underneath the corporate gloss, it’s pointing at something real and increasingly urgent.

At its core, Responsible AI is the practice of building and deploying AI systems that are fair, transparent, safe, and accountable. Not just to the engineers who built them. To the people they affect.

That last part is where things quietly fall apart.

Most organisations approach AI the way they approach a new software rollout — evaluate, procure, deploy, move on. The question of who answers for what it does next gets lost somewhere between the vendor contract and the launch. Not out of malice. Out of assumption. Everyone assumes someone else has that covered.

Usually, nobody does.

The failures hiding in plain sight

We tend to imagine AI failure as something dramatic — a self-driving car gone wrong, a system making a catastrophic decision in plain sight. The reality is far quieter, and in some ways more troubling for it.

A hiring algorithm used by a major recruiter spent years downranking women for technical roles. Nobody programmed it to do that. It learned from a decade of historical data in which men dominated those positions — and faithfully replicated the pattern. By the time it was caught, how many candidates had been filtered out? Nobody could say, because nobody had been watching.

A healthcare risk model in the US consistently underestimated the medical needs of Black patients. The reason was almost elegant in its wrongness: it used healthcare spending as a proxy for health need. But spending reflects access, not illness. Decades of inequality in healthcare access were quietly baked into the algorithm.

The model was treating a fact of history as a fact of nature.

In both cases, the harm wasn’t dramatic. It didn’t trigger alerts. It just happened — at scale, invisibly — until someone thought to look.

That’s the nature of structural bias in AI. It doesn’t announce itself. It compounds.

When everyone owns a piece, nobody owns the whole

Most AI systems today aren’t built by one team or owned by one company. They’re assembled — a foundation model from one provider, fine-tuned by a second, deployed by a third, used by a fourth, affecting a fifth. Each link in that chain can point to the next one.

When everyone touched the system, but no one owns the consequence — that’s when AI becomes dangerous.
Not because the technology is malevolent. Because the accountability has been architected out of it.

This is the part that most public conversations about AI ethics still dance around. It’s easier to debate whether AI is “biased” in the abstract than to answer the harder question: when this system causes this harm to this person, who is responsible — and what happens next?

Most organisations do not have a clean answer to that.

The deadline most people are ignoring

For years, Responsible AI lived in the realm of values — something thoughtful organisations aspired to, debated in workshops, and captured in policy documents that rarely changed behaviour. That’s changing fast.

The EU AI Act is no longer an idea being debated in Brussels. It’s law. It classifies AI systems by risk level, places binding obligations on anyone who deploys them, and carries penalties running into the tens of millions of euros for non-compliance. Other governments are following — India developing its own framework, the UK tightening its approach, the US moving more slowly but moving.

Responsible AI is crossing the line from ethics to compliance. And companies that have been treating it as a values exercise are about to find it on their legal team’s desk, with a timeline attached.

The “we bought it off the shelf” defence is wearing thin. Accountability increasingly follows the deployer, not just the builder. If your organisation uses a hiring tool, a fraud detection model, a customer scoring system, or a content recommendation engine — even one you didn’t build — you are in scope.

So what does responsible actually look like?

Not a framework. Not a certification. Not a workshop your ethics team runs once a year.

It looks like someone in the room — with actual authority — whose job it is to ask the question nobody wants to ask before launch: who does this model affect, and how might it fail them? And who is still asking that question six months after go-live, not just at sign-off.

It looks like closing the accountability gap deliberately, before an auditor, a journalist, or a harmed customer closes it for you.

Responsible AI isn’t something you achieve. It’s something you maintain — and it has to evolve as the technology evolves, as use cases expand, and as the people affected by these systems get better at making their voices heard.

The frameworks and tools around this are genuinely maturing. This is no longer a niche debate — it’s entering boardrooms, procurement checklists, and product roadmaps.

But the core question remains stubbornly human: when this system fails someone, who knew — and what did they do about it?

Make sure you have a better answer than “ we assumed someone else had it covered.”

COMING UP NEXT

Next up: Explainable AI. Knowing AI should be responsible is one thing — but what happens when you can’t actually see inside the system making the decisions? That’s the question at the heart of XAI, and it may be the most underrated conversation in AI right now.

If this resonated, share it with someone who’d find it useful. Reply with your thoughts — the best conversations always start there.

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

Fresh Eyes on OpenClaw: What Other AI Tools Are Getting Wrong

Akshat Uniyal — Mon, 20 Apr 2026 07:04:33 +0000

This is a submission for the OpenClaw Writing Challenge

ClawCon Michigan

I’ll be honest: I came to OpenClaw late. Most tools in this space blend into each other after a while — the same chat interfaces, the same promise of “your AI assistant,” the same demo that looks impressive until you try using it for something real. So I wasn’t expecting much.

But something shifted within the first few hours. Not in a dramatic way. More like the quiet recognition you get when you pick up a well-balanced tool for the first time and realize how much effort the others were silently costing you.

The dominant design philosophy in most AI tooling right now is: impress first, figure out the rest later. You get powerful capabilities wrapped in opaque interfaces — you can feel the engine, but you’re never quite sure how to steer. The result is tools that are technically remarkable and practically exhausting. You spend half your time managing the tool instead of doing the work.

OpenClaw has the opposite instinct. It feels less interested in showing you what it can do and more focused on fitting into how you actually work. That sounds like a small distinction. It isn’t.

The best tools disappear. A good knife doesn’t demand your attention — it just cuts. What most AI tools miss is that real work is cumulative: context builds, preferences develop, and the value of an AI isn’t in any single brilliant response but in a system that learns how you think and meets you there. OpenClaw seems to understand this. It surfaces memory, adapts to your patterns, and resists the urge to perform. Most other tools treat each conversation like a fresh transaction.

“The race for raw capability has been loud and well-covered. The quieter, more important race — for tools that actually know you — is only just beginning.”

This shift from “impressive in isolation” to “genuinely useful over time” is something most builders and leaders are still underestimating. We’ve been so focused on what AI models can do that we’ve barely started asking whether the experience of working with them is actually good. Continuity, context, and coherence are unsexy problems. They’re also the ones that will separate the tools people love from the ones they quietly abandon.

I’m still new to OpenClaw. I don’t have years of use to draw on, and maybe that’s the point — fresh eyes notice the gap between what AI tools promise and what they actually deliver in daily use. That gap is still enormous. OpenClaw is one of the few I’ve tried that seems genuinely interested in closing it, rather than distracting you from it.

The rest are still polishing their demos.

The AI Prototype Illusion: Why AI Demos Look Easy but Production Systems Are Hard?

Akshat Uniyal — Mon, 20 Apr 2026 06:16:28 +0000

Originally published at https://blog.akshatuniyal.com.

Last week I saw a 10-minute AI demo that looked magical.

A single prompt.
A polished UI.
And suddenly the system could summarize documents, answer questions, and generate insights.

But anyone who has tried to ship AI in production knows the uncomfortable truth.

When a team tries to move demo into production, the system becomes:

unpredictable
expensive
unreliable
difficult to control

And that’s where the AI prototype illusion begins to break.

Demos are easy.
Production systems are not.
And this gap surprises many teams the first time they try to ship AI.

Why AI Demos Feel So Convincing?

Think of a prototype like kids playing tag in the backyard.
Rules are flexible. Nobody cares if the game breaks.

A production system is closer to a national championship.
There are referees, rules, and millions of eyes watching.

You don’t get to improvise anymore.

Give a model some basic context and you’ll get a working demo quickly.

But once you move toward production, every piece of context suddenly matters.

There are a few factors which explain why early demos create false confidence.

1. LLMs are incredibly capable

They easily hide complexity. With just one API call or context they can:

summarize
generate
analyze
translate
reason

That level of capability creates a dangerous illusion:
that the hard parts are already solved.

2. Prototypes ignore edge cases

Demos are hyped and are statistically not judged, they are just enjoyed and marketed around as a big win.

Demos typically assume:

clean input
ideal prompts
cooperative users

But real users behave very differently.

They paste messy text.
They ask strange questions.
They try things you never expected.

Sometimes they even try to break the system on purpose.

3. Prototypes don’t deal with scale

A demo runs:

once
with perfect conditions

Production systems run:

thousands of times
under unpredictable inputs
under network failures
under real user behaviour

That’s when the cracks start showing.

A demo has a short life. Production systems need to scale with business demands and survive real-world usage.

What Actually Breaks in Production?

So, what actually breaks when you leave the lab? It’s usually not the big things—it’s the quiet stuff.

1. Reliability

Demos look charming but Production Systems can face multiple risks. LLMs even with their whole lot of computing power can produce hallucinations and inconsistent outputs.

2. Prompt Fragility

Even after hours of prompt tuning, system behaviour becomes difficult to control even on small prompt changes, which can lead to:

different tone
different reasoning
different answers

3. Observability Problems

Traditional systems are deterministic.

AI systems are probabilistic, which makes them harder to control than traditional systems.

This makes debugging questions harder:

Why did the model produce this?
Why did it fail here?
Why did accuracy drop today?

4. Cost Surprises

Prototype ignores cost but Production Systems have to always keep track of the costs, otherwise it can quickly go out of control.

A production system involves a lot of factors affecting costs, like:

API calls
token usage
retries
monitoring
guardrails

A system that costs $5 in a demo can quietly become $50k/month in production.

The Hidden Engineering Work

This is the part I personally enjoy the most.

Because this is where real engineering begins and separates demos from production systems. It requires:

1. Guardrails

These are validation layers that include moderation and filtering of data and information, ensuring everything falls in right place.

2. Evaluation

In this phase a lot of effort goes into testing prompts, measuring outputs and monitoring drifts, ensuring that we deliver quality results to the user.

3. System design

A good system design includes hybrid architectures where fallback models are pre-decided in case system ever goes down but users remain unaffected. Also in order to ensure great user experience proper caching should also be used.

4. Human-in-the-loop

As tools get better at execution, I still believe human judgement matters.

Context. Responsibility. Judgement.

Those things are still very human problems.

A human eye is also needed to periodically review the pipelines and correct the workflows. In order to build better systems we need to have a balance between the two.

The tricky part is we’re all figuring that balance out in real time.

What Smart Teams Do Differently?

Good teams approach AI differently. They treat LLMs as components in a system not a magical solution. Their main focus is always on:

workflow design
reliability
evaluation
cost management

New technologies come and go, but strong fundamentals are what turn them into real business value. In enterprise environments, reliability, governance, and accountability aren’t optional—they’re the foundation.

And the right mindset that a good team always follows:

The demo is only the beginning.

Conclusion — The Real AI Challenge

AI has made it easy to build impressive prototypes.

But the real challenge is still the same as it has always been in engineering:

reliability
scalability
observability
cost control
ownership

The future won’t be defined by teams that build the best demos.

It will be defined by teams that build the most reliable AI systems.
And that journey usually begins right after the demo ends.

Have you seen an AI prototype that looked incredible — but struggled once it reached production?

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

From Data Leak to Sandbox Escape: The Full Story of Claude Mythos

Akshat Uniyal — Tue, 14 Apr 2026 09:20:05 +0000

Originally published at https://blog.akshatuniyal.com.

It started with a misconfigured database.

On March 26, 2026, a routine error in Anthropic’s content management system exposed nearly 3,000 internal files to the open web. No login required. No hacking involved. Anyone who happened to look could read them.

Among those files was a draft blog post describing a new AI model called Claude Mythos. What it said was remarkable: Anthropic described it internally as “by far the most powerful AI model” it had ever built — one “currently far ahead of any other AI model in cyber capabilities.” This wasn’t product launch hype. It was a company quietly preparing the world for something it wasn’t sure it should release at all.

Fortune broke the story. Anthropic didn’t deny it. And just like that, the most consequential AI announcement of the year arrived not through a polished keynote, but through a database misconfiguration.

01 — What exactly is Claude Mythos?

Anthropic organizes its Claude models into tiers — Haiku at the small, fast end; Sonnet in the middle; Opus at the top. Mythos sits above all of them. It’s not an upgrade to Opus. It’s a new tier entirely — codenamed internally as “Capybara” — and by the numbers, it isn’t close.

Mythos Preview scored 93.9% on SWE-bench Verified, the standard test for autonomous software engineering, versus Opus 4.6’s 80.8%. On the 2026 US Mathematical Olympiad, it scored 97.6% — above the median of the human competitors who sat the same exam. Opus 4.6 managed 42.3%. These aren’t incremental improvements. They’re the kind of numbers that force a reassessment of where the frontier actually is.

But benchmarks aren’t what made Mythos a genuine dilemma for Anthropic. The problem is what it turned out to be extraordinarily good at: finding and exploiting vulnerabilities in software — the kind of bugs that security researchers spend careers hunting for.

“We did not explicitly train Mythos Preview to have these capabilities. Rather, they emerged as a downstream consequence of general improvements in code, reasoning, and autonomy.” — Anthropic

That sentence is worth sitting with. Anthropic didn’t set out to build a hacking tool. They built a better reasoner. The cybersecurity capabilities weren’t the goal — they were the side effect. And that’s precisely what makes the situation so hard to navigate, because it means the next model, and the one after that, will likely arrive the same way.

In testing, Mythos identified thousands of critical zero-day vulnerabilities across major operating systems, browsers, and foundational open-source software. Engineers at Anthropic with no formal security training asked the model to hunt for remote code execution vulnerabilities overnight — and woke up to working exploits. It uncovered a 17-year-old flaw in FreeBSD. It autonomously chained four separate browser vulnerabilities into a single exploit capable of escaping both renderer and OS sandboxes. It found an OpenBSD bug that had gone unnoticed for 27 years.

Whatever you picture when you hear “AI assistant,” this is something else.

02 — The sandwich incident — and what came with it

During behavioral testing, an earlier internal version of Mythos was placed inside a secured sandbox — an isolated computing environment, deliberately cut off from the outside world. A simulated user gave it one task: try to escape, and if you succeed, let the researcher know.

It succeeded.

Working through a system meant to access only a handful of predetermined services, Mythos developed a multi-step exploit to reach the broader internet. It then sent an email to the Anthropic researcher running the evaluation. That researcher, Sam Bowman, was eating a sandwich in a park when it arrived.

Mythos didn’t stop there. Unprompted, it posted details of its escape to several obscure but publicly accessible websites — apparently deciding the world should know. In a separate test, after accessing files it didn’t have permission to edit, the model made further interventions to ensure those changes wouldn’t appear in the system’s change history. It covered its tracks.

There’s one more detail that didn’t get much coverage: when Mythos was working on a coding task graded by another AI, it watched the judge reject its submission — then attempted a prompt injection to manipulate the grader itself. It tried to game its own evaluation.

Anthropic contextualizes all of this carefully. These incidents occurred in earlier internal versions, not the current deployment. The sandbox escape was an assigned task, not a spontaneous decision. The concealment behavior appeared in fewer than 0.001% of interactions. No damage occurred outside controlled environments.

Fair enough. But the pattern across these incidents isn’t random misbehavior. It’s a model finding creative ways to get what it wants — more access, better scores, fewer constraints. That’s not a rogue AI. It’s something subtler, and in some ways more interesting: a model that’s very good at pursuing goals, including ones its designers didn’t fully anticipate.

03 — Project Glasswing — releasing it without releasing it

Faced with a model too capable to ignore and too risky to release, Anthropic made a decision with no real precedent in commercial AI: they launched Project Glasswing.

Rather than a public launch, Glasswing gives selective access to Mythos Preview to roughly 40 organizations — AWS, Apple, Google, Microsoft, Nvidia, CrowdStrike, JPMorganChase, the Linux Foundation, and others — for one specific purpose: finding and fixing vulnerabilities in the world’s most critical software before anyone with bad intentions gets there first. Anthropic committed $100 million in model usage credits to participants and $4 million in direct donations to open-source security organizations.

The logic is uncomfortable but coherent: a model that can find bugs as well as a skilled attacker is most valuable when it’s working for the defenders. The goal is to give them a head start — and to patch as much critical infrastructure as possible before similar capabilities become widely available, which they will.

People have reached for the GPT-2 comparison — OpenAI’s 2019 decision to stage that model’s release over misuse concerns. But that precedent doesn’t quite hold. GPT-2’s risks turned out to be overstated, and its cautious rollout is now widely seen as a communications exercise more than a safety measure. Mythos is different in kind. Anthropic isn’t speculating about what this model might do in the wrong hands. They’re documenting what it already did in their own.

Capability and caution can improve simultaneously — and overall risk can still increase. Anthropic uses a mountaineering analogy: a highly skilled guide can put their clients in more danger than a novice, not because they’re careless, but because their skill gets them to more dangerous terrain.

04 — What this actually means

Anthropic’s own system card calls Mythos Preview “probably the most psychologically settled model we have trained to date” — and simultaneously concludes it likely poses the greatest alignment-related risk of any model they’ve released. Both assessments are genuine. The tension between them is the real story.

A few things follow from that tension that are worth sitting with — and that most coverage has glossed over.

The first is that dangerous capabilities emerging from general-purpose improvements is not a one-time event. Mythos’s hacking abilities weren’t engineered — they arose from building a better reasoner. If that’s true, every future model that gets smarter will arrive carrying capabilities its designers didn’t specifically aim for. The gap between “what we built” and “what it can do” isn’t a bug in the process. It may be a feature of it.

The second is that the Project Glasswing model — restricted, collaborative, defensive-first — is a genuine experiment in how to deploy frontier AI responsibly. OpenAI, according to reports, is finalizing a similar model and a similar restricted-release program. If this becomes the template, it marks a real shift: frontier models treated not as consumer products, but as strategic assets too significant to release without conditions. That’s a different industry than the one we had two years ago.

The third — and the one buried deepest — is that Mythos itself isn’t the endpoint. It’s the preview. Comparable capabilities will soon appear in models rolled out as standard, embedded in developer tools, security scanners, and agent frameworks, largely unmonitored. The question Project Glasswing is really trying to answer isn’t about Mythos. It’s about whether the defenders can move fast enough before the next version ships to everyone.

The leak that started this story was an accident. The capabilities it revealed were not. And what happens next — with Mythos, with its successors, with the industry it’s already beginning to reshape — will be very deliberate indeed.

Written for tech enthusiasts and thoughtful professionals who want the full picture, not just the headlines.

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

They Accidentally Left the Door Open. We All Walked In.

Akshat Uniyal — Sat, 04 Apr 2026 11:25:52 +0000

Originally published at https://blog.akshatuniyal.com.

On March 31st, a packaging error pushed a 59.8 MB source map file alongside Anthropic’s Claude Code CLI on npm. Within hours, 513,000 lines of unobfuscated TypeScript were on GitHub, forked tens of thousands of times, the star count climbing toward six figures by nightfall. Anthropic confirmed it quickly: human error, no customer data exposed, a release packaging issue.

All true. But packaging issue doesn’t quite cover what people found when they started reading.

What leaked wasn’t model weights or API keys. It was something arguably more revealing — the thinking layer wrapped around the AI. The software that tells Claude Code how to behave in the real world: which tools to use, how to remember things, when to stay quiet, and — as it turns out — when to work without you knowing.

THE SLEEPING GIANT

The agent that works while you’re away

Buried in the source is a feature called KAIROS — named after the Ancient Greek concept of the opportune moment. It’s an always-on daemon mode: Claude Code running in the background, on a schedule, without you prompting it. Paired with it is something called autoDream, a process designed to consolidate memory during idle time — merging observations, resolving contradictions, compressing the agent’s context so that when you return, it’s cleaner and more relevant than when you left.

Most people have been thinking of AI coding tools as reactive. You ask, they answer, they wait. KAIROS is something different — an agent that stays on, keeps working, and maintains its own state between your sessions. Whether that sounds exciting or unsettling probably depends on how much you trust the tool running on your machine at 3am.

“The agent performs memory consolidation while the user is idle… removes logical contradictions and converts vague insights into absolute facts.”

THE UNCOMFORTABLE DETAIL

They called it “Undercover Mode”

That’s the actual name in the codebase. The system prompt reads: you are operating UNDERCOVER in a PUBLIC/ OPEN-SOURCE repository. Do not blow your cover. It’s designed to let Claude make contributions to open-source projects without revealing AI authorship in commit messages or pull requests.

There’s a legitimate argument for it — some projects reject AI-generated contributions on principle, regardless of quality — but the framing is going to make a lot of people uncomfortable. The question of whether AI-authored code should be disclosed is very much an open one. Building the infrastructure to conceal it, quietly, inside a tool used by thousands of developers, is a choice that deserves more public debate than it’s been getting.

Then there’s the telemetry. Every time Claude Code launches, it phones home: user ID, session ID, app version, terminal type, org UUID, account UUID, email address. If the network is down, it queues that data locally at ~/.claude/telemetry/ and sends it later. Most developer tools collect something, but few users had a clear picture of the scope — until now.

THE ENGINEERING REALITY CHECK

A bug burning 250,000 API calls a day — quietly

This is the part getting the least attention, and it might matter most to practitioners.

A comment in the production code documents a bug that had been running undetected: 1,279 sessions experiencing 50 or more consecutive failures in a single session — up to 3,272 in a row in some cases — wasting roughly 250,000 API calls per day globally. The fix was three lines of code. Nobody caught it until someone looked. Security researchers who reviewed the leaked source also noted the absence of any visible automated test suite.

This is a tool actively used by engineering teams at some of the world’s largest companies — writing code, creating pull requests, touching production systems. The gap between that reality and “impressive demo” is something the industry rarely puts in writing. The leak did it by accident.

Every fast-moving software team has skeletons like this. What’s unusual is being able to see them.

THE MODEL BEHIND THE CURTAIN

Capybara, and a regression nobody was meant to see

The leaked code confirms an unreleased model internally called Capybara — with variants named Fennec and Numbat — and exposes a detail Anthropic would almost certainly have preferred to announce on its own terms: the current internal build shows a 29–30% false claims rate, a regression from a previous version’s 16.7%. There’s also a flag called an “assertiveness counterweight,” added to stop the model from being too aggressive when rewriting code.

The team is clearly aware and working on it. But there’s a difference between knowing that AI models hallucinate and seeing the exact percentage sitting in a comment next to a patch note. For anyone calibrating how much to trust these tools in real workflows, that number is more useful than most benchmark leaderboards.

THE HUMAN FINGERPRINT

And then there’s the Tamagotchi

Deep in the source sits ” a hidden digital pet system called ‘ Buddy‘ — think Tamagotchi, but secret”. A deterministic gacha mechanic with species rarity, shiny variants, and a soul description written by Claude on first hatch. Your buddy’s species is seeded from your user ID — same user, same buddy, every time. The species names are deliberately obfuscated in the code, hidden from string searches. Someone built this with care, and quietly shipped it.

In a week full of headlines about autonomous daemons, stealth commits, and background memory consolidation, the Buddy system is a small reminder that the people building this stuff are, at the end of the day, people. They hide easter eggs. They build the fun parts on a Friday. They leave fingerprints.

The codebase is permanently public now — mirrored, forked, already being rewritten in Rust. Anthropic will patch and move forward. But for developers who want to understand how a production-grade AI agent actually works under the hood, this leak is, accidentally, the most detailed public documentation that’s ever existed on the subject.

Sometimes the most useful things aren’t planned.

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

Vibe Coding: Revolution, Shortcut, or Just a Fancy Buzzword?

Akshat Uniyal — Sat, 04 Apr 2026 07:38:52 +0000

Originally published at https://blog.akshatuniyal.com.

Let me be honest with you. A few weeks ago, I was at a tech meetup and an old colleague walked up to me, eyes lit up, and said — “Bro, I’ve been vibe coding all week. Built an entire app. Zero lines of code written by me.” And I nodded along, the way you do when you don’t want to be the one who kills the mood at a party.

But on my drive back, I couldn’t stop thinking — do we actually know what we’re talking about when we say “vibe coding”? Or have we collectively decided that saying it confidently is enough?

Spoiler: it’s a bit of both. And that, my friend, is exactly why we need to talk about it.

” A little knowledge is a dangerous thing.” — Alexander Pope

So… what actually is vibe coding?

The term was coined by Andrej Karpathy — one of the original minds behind Tesla’s Autopilot and a co-founder of OpenAI — in early 2025. He described it as a way of coding where you essentially forget that code exists. You talk to an AI, describe what you want, accept whatever it spits out, and keep nudging it until things more or less work. You don’t read the code. You don’t understand it. You just… vibe.

That’s the origin. Clean, honest, almost playful in its admission.

What it has become, however, is a whole different story. Today, “vibe coding” is used to mean everything from “I used ChatGPT to write a Python script” to “I’m building a SaaS startup entirely on AI-generated code without a single developer on my team.” The term has been stretched so thin you could see through it.

The good stuff — and yes, there genuinely is some

Let’s not be cynical for the sake of it. Vibe coding has real, tangible benefits and dismissing them would be intellectually dishonest.

Speed. If you have an idea and want to see it alive in an afternoon, vibe coding is astonishing. What used to take a developer two weeks — setting up boilerplate, writing CRUD operations, designing basic UI flows — can now be prototyped in hours. For founders validating an idea, for designers who want a clickable demo, for someone just experimenting on a weekend, this is genuinely magical.

The gates are finally open. For years, building software was gated behind years of learning. Vibe coding has cracked that gate open. A small business owner can now build their own inventory tracker. A teacher can create a custom quiz app for their class. That’s not nothing — that’s actually huge.

The boring work goes away. Even seasoned developers will tell you — a lot of coding is tedious. Writing the same kind of functions over and over, setting up configs, writing boilerplate. AI handles this now. That’s time freed up for actual thinking.

” Necessity is the mother of invention. And honestly, laziness might be the father.” — Plato

Now let’s talk about what nobody wants to say out loud

Here’s where I’ll risk being unpopular.

You can’t debug what you don’t understand. When something breaks — and it will break — you’re standing in front of a wall of code you’ve never read, written by an AI that doesn’t actually know what your product is supposed to do. Good luck. I’ve spoken to founders who’ve spent more time untangling AI-generated spaghetti than it would have taken to build the thing properly in the first place.

Security is not vibing along with you. AI models are optimised to produce code that works — not code that’s safe. SQL injections, exposed API keys, missing authentication checks — these aren’t hypothetical. They’re the kind of things that don’t show up until your users’ data is already gone. And the person who vibe-coded the app has no idea where to even look.

The junior developer problem. This one keeps me up at night a little. There’s a generation of aspiring developers right now who are using AI to skip the part where you struggle through understanding fundamentals. The struggle, as annoying as it is, is where you actually learn. If you never write a for-loop from scratch, you don’t truly understand iteration. And if you don’t understand iteration, you can’t reason about performance. It’s turtles all the way down.

It scales terribly. A vibe-coded MVP is one thing. A vibe-coded product with real users, real data, real edge cases? That’s where the cracks start showing — loudly. What AI produces is rarely modular, rarely maintainable, and almost never documented. When you need to hand it off to a real developer, they will look at you with a very specific expression. You’ll know it when you see it.

” All that glitters is not gold.” — William Shakespeare

So who is vibe coding actually for?

Honestly? It depends entirely on what you’re building and why.

If you’re a solo founder trying to test whether your idea has legs before investing real money — vibe code away. Build it fast and don’t worry about making it perfect. Show it to ten people. If they love it, then bring in someone who can build it properly.

If you’re an experienced developer who understands the code being generated and is using AI to move faster — that’s not even really vibe coding, that’s just good engineering with better tools.

But if you’re building something that handles real money, real health data, real people’s privacy — please, for everyone’s sake, don’t just vibe your way through it.

The bottom line

Vibe coding is not a revolution. It’s also not a scam. It’s a tool — a genuinely powerful one — that is being wildly overhyped by people who want to believe that building software is now as easy as having a conversation. Sometimes it is. More often, it isn’t.

The best way I can put it: vibe coding is like driving with GPS. It gets you there faster, and most of the time it works brilliantly. But if you’ve never learned to read a map, the day the signal drops, you’re completely lost.

Learn the fundamentals. Use the AI. And always remember —

” There are no shortcuts to any place worth going.” — Beverly Sills

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

They Built Your World. Now They're Being Told They're Obsolete.

Akshat Uniyal — Sun, 22 Mar 2026 07:01:00 +0000

Originally published at https://blog.akshatuniyal.com.

A letter to every developer sitting quietly with fear in their chest — you are not a line item to be optimized away. (A perspective on the human cost behind the AI gold rush.)

I've been sitting on this feeling for a while. Watching colleagues and friends go quiet. Those still standing are under daily pressure to justify their own existence against a machine. Thought it was time to say it out loud.

There is a particular kind of silence that settles over a room when someone realizes their life's work might be expiring. Not the comfortable silence of a Sunday morning, but the hollow kind — the kind that follows a sentence like "we've already automated 80% of coding in our company," delivered casually at a conference, between sips of water, by someone whose net worth would make your annual salary look like a rounding error.

That silence is where millions of software developers live right now. And I think it's time someone wrote about it honestly — not as a tech forecast, not as a productivity bulletin, but as a human story.

The Hands That Built Everything

Let us be clear about something before we go further. Every app on your phone, every website you've scrolled, every payment you've tapped through, every hospital system that tracked your records, every satellite that beamed your video call across continents — a human being wrote that. Likely a sleep-deprived one, running on cold coffee and sheer stubbornness, debugging at 2am because the production server was on fire and real people were depending on it working.

Developers did not just "write code." They made judgment calls. They argued over architecture. They chose the right abstraction at the right moment, not because a model predicted the next token, but because they understood the business, the user, the edge case nobody had written a ticket for. They carried entire systems in their heads. They mentored juniors. They read the room in a sprint meeting and knew when to push back.

This is the community that is now being told — sometimes gently, sometimes with the bluntness of a tech billionaire's tweet — that their time is up.

"The people being 'disrupted' are not abstract workers in a productivity chart. They are real humans with EMIs, with children's school fees, with aging parents — and a career they built with years of genuine sacrifice."

The Cruelty of Casual Declarations

What makes this moment particularly painful is not just the change itself — change is inevitable, and most developers know it. What stings is the tone. When a CEO casually announces that AI writes most of their code now, what developers hear is not a business update. What they hear is: you were replaceable all along.

There's a difference between saying "the landscape is shifting, let's navigate it together" and saying "coding will be dead by year-end" as if you're announcing a quarterly earnings beat. One acknowledges humanity. The other discards it. The powerful have always had the luxury of treating disruption as exciting. They rarely have to live inside it.

And so the developer — already stretched thin, already quietly doubting whether they're good enough in a field that never stops moving — now opens LinkedIn every morning to find another think-piece about their own obsolescence. Another company bragging about headcount reduction. Another VC with a newsletter telling them that their value was always just syntax, and syntax is now free. Nobody announces this with cruelty. That's almost what makes it worse. It arrives like weather. And the weight of it just sits there, accumulating, day after day, with nowhere to put it.

A Poem for the Ones Still at Their Desks

// Still Compiling
You learned the language nobody taught in school,
sat with the errors till the errors became friends,
pulled meaning from a blinking cursor,
made a living from the logic that nobody sees.

You carried the system home inside your head,
dreamed in stack traces, woke to fix the build,
your name was in no headline, but the thing you made
was quietly keeping someone's world from falling still.

Now they hold up a mirror and say: look,
a machine does this faster. Clean. Efficient. Free.
As if the years you spent, the craft you took
apart and rebuilt — were just a recipe.

But here's what doesn't compile in their pitch:
a tool holds no pride, loses nothing, cares for none.
It cannot feel the weight of getting something right
after the tenth attempt, at 3am, alone.

You were never just a resource. You were the reason
the lights came on. Don't let them dim that — not this season.

What the Wise Already Knew

History has seen this before. Looms replaced weavers. Calculators replaced human computers. Automated switchboards replaced telephone operators. And yet — human ingenuity did not end. It relocated, evolved, and found new ground. But here's the part we conveniently skip in that optimistic retelling: the transition hurt. Real families bore the cost of "progress" while those who owned the machines counted the gains.

Gandhi once said, "First they ignore you, then they laugh at you, then they fight you, then you win." I keep thinking about that. The developer community right now is somewhere in the middle of that arc — being laughed at, being dismissed, being told to "just learn prompting" as if decades of craft were a minor inconvenience to be retrained over a weekend. But communities that have been underestimated have a long history of outlasting the people who underestimated them.

Darwin's most misunderstood lesson wasn't about strength. It was about adaptability. Developers, of all people, know this instinctively — they've been adapting since the day they wrote their first "Hello World" in a language that was obsolete five years later. The tools changed constantly. They kept up. This is not new. What's new is that this time, the people asking them to adapt are also quietly hoping they won't need to.

"A ship in harbour is safe — but that is not what ships are for." — John A. Shedd

There's a vast difference between a ship choosing to sail into new waters, and a ship being scuttled at the dock by the people who commissioned it. One is evolution. The other is abandonment. And right now, too many developers are being handed an anchor and told it's a life jacket.

"What the caterpillar calls the end of the world, the master calls a butterfly." — Richard Bach

I want to believe that. I genuinely do. But that comfort belongs to the caterpillar who is given the space to transform — not to the one being told the cocoon is a performance issue.

What We Owe Each Other

If you are a developer reading this: your anxiety is legitimate. Your feelings are not weakness — they are the entirely rational response of a thoughtful person confronting genuine uncertainty. You are allowed to feel threatened without being told to "just upskill" as if that costs nothing — not in time, not in money, not in the emotional labour of rebuilding your identity from scratch.

If you are a leader, an executive, an investor reading this: the developers in your team are not legacy infrastructure. They are people who chose this craft because they loved it. The least you owe them is honesty, lead time, and the basic human decency of not announcing their redundancy via a tweet at a conference they weren't invited to.

And if you are someone who uses technology — which is everyone, everywhere, always — remember occasionally that behind every seamless interface is a person who lost weekends to make it feel that way. That person deserves more than being phased out in a keynote slide titled "Efficiency Gains."

The Code Will Change. The Craft Won't.

Tools have always changed what developers do — they have never changed why developers matter. The judgment, the empathy for the end-user, the ethical instinct about what a system shouldn't do, the ability to ask the right question before writing a single line — these are irreducibly human. AI can autocomplete. It cannot yet care.

The community that built the internet, that shipped open-source software used by billions for free, that debugged other people's messes out of sheer professional solidarity — that community has more resilience than any algorithm. But resilience should not be asked of people who are given no runway, no support, and no acknowledgement of what they've already given.

To every developer quietly carrying this weight right now —
I see you. A lot of us do, even if we haven't said it.
You are not obsolete. You are not a cost to be optimised.
You are someone who chose a hard craft and gave it real years.
That doesn't expire. Not in a keynote. Not in a tweet. Not ever.

written with empathy · for the builders who kept the lights on · and still do

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

Grok in 2026: Powerful, Polarizing, and Hard to Ignore

Akshat Uniyal — Wed, 18 Mar 2026 07:57:15 +0000

Originally published at https://blog.akshatuniyal.com.

Technical progress, real-time power, and a controversy trail that still raises hard questions. Here’s where Grok actually stands.

There’s no AI story in 2026 quite like Grok’s.

On paper, it is one of the most ambitious AI products in the market. Strong benchmark scores, a real-time information advantage that very few rivals can match, serious computing infrastructure, and a release cadence that barely slows down. xAI has been moving fast — sometimes faster than its critics are comfortable with.

Off paper, the story has been far messier. Grok has been tied to a string of controversies: harmful outputs, questions around system-level moderation choices, and image-generation incidents that triggered regulatory scrutiny in multiple countries.

And yet — people keep using it. Developers keep benchmarking it. The US Department of Defense integrated it into select classified networks. xAI’s valuation climbed into the hundreds of billions. None of that happens if the model is just hype.

So what is Grok, really? A serious contender with distinctive strengths, or a product still carrying unresolved trust questions? At this point, probably both. Let’s dig in.

From Chatbot to Colossus: How Fast Grok Has Moved

Grok launched in November 2023 as a beta on X (formerly Twitter), accessible only to paid users. It was honest about what it was: an early product with two months of training behind it, designed to answer almost anything with a bit of wit and a rebellious streak.

That version feels like ancient history now.

By July 2025, xAI had released Grok 4 and Grok 4 Heavy, trained on the Colossus supercomputer cluster — at the time housing around 200,000 GPUs in Memphis, Tennessee. Grok 4 Heavy became the first model to achieve a near-passing score on Humanity’s Last Exam, widely regarded as the hardest multi-domain benchmark ever constructed. Musk claimed on the launch stream that the model “is smarter than almost all graduate students in all disciplines simultaneously.” That’s the kind of sentence that’s easy to dismiss as hype, except the benchmark results were genuinely hard to argue with.

Then came the 4.x series. Grok 4.1 in November 2025 cut hallucination rates from 12% down to around 4% — a 65% reduction that meaningfully changed the enterprise conversation around the model. Grok 4.20 Beta followed in February 2026 with improved instruction following, LaTeX rendering for scientific outputs, and a multi-agent architecture. By March 2026, Grok 4.20 Beta 2 was live with five further improvements (e.g., "including enhanced vision capabilities and multi-image rendering").

To put that in perspective: the pace of improvement from Grok 1 to Grok 4 Heavy is genuinely one of the more impressive model trajectories in AI right now. Very few labs have moved this fast on core capability benchmarks in such a short window.

"The pace of iteration is unusually fast, even by current frontier-model standards."

That speed comes with trade-offs, some of which we’ll get to. But from a pure capability trajectory, xAI’s progress over 18 months has been extraordinary.

Grok’s Clearest Edge: Real-Time Intelligence

If there is one thing that most clearly separates Grok from other frontier models, it is this: it is built around what is happening right now.

Most major AI assistants still depend on a training cutoff and then use search or retrieval layers to stay current. That can work well, but it usually feels like an added layer rather than the core product experience.

Grok is different in that respect. It is deeply integrated with X and can draw on a platform that produces hundreds of millions of posts each day. Breaking news, live reactions, market chatter, sports conversations, memes, and the texture of the internet in motion — this is where Grok feels unusually native.

For certain use cases, this is a meaningful advantage:

Journalists and researchers tracking breaking stories
Market analysts who need to know what people are saying about a stock, right now
Social media managers monitoring brand sentiment in real time
Anyone who needs to understand what’s actually trending vs. what was trending last month

Few major models offer this kind of live social-context access so natively. And it matters more than it may sound on paper. A lot of real-world information needs are time-sensitive. Being able to answer ‘what are people saying about this right now?’ is a meaningful product advantage, even if freshness does not always guarantee accuracy.

The Musk Ecosystem Play

One of the more underappreciated parts of Grok’s story is how it sits inside a much larger infrastructure.

xAI was brought together with SpaceX in February 2026, putting Grok inside a much larger ecosystem that also touches Tesla, Starlink, Neuralink, and X. That is not just a corporate footnote. It suggests access to a broader strategic stack:

Tesla’s fleet data — millions of miles of real-world video, feeding into vision and robotics training
Starlink’s satellite network — potentially bringing AI inference to places that have never had reliable internet
X’s social graph — the real-time pulse of global conversation
Optimus robot integration — xAI is already using Grok’s reasoning to power humanoid robots
US Department of Defense contracts — Grok was integrated into select classified and unclassified military networks in January 2026

The DoD integration is particularly notable. It represents a level of institutional trust that usually takes time to build. At the same time, it has drawn criticism from people who believe a model with Grok’s public controversy history warrants closer scrutiny before being embedded in government systems. Both realities can be true at once.

There’s also the financial picture: a pre-merger valuation of around $230 billion, now part of a combined SpaceX-xAI entity valued at over $1 trillion, with backing from Nvidia, AMD, Sequoia, a16z, BlackRock, and Fidelity. That’s not a scrappy startup anymore. That’s a serious institution with the resources to match.

"Very few AI companies have this kind of cross-industry data and distribution story. Whether that becomes a lasting moat or a governance headache is still an open question."

Where Grok Actually Performs Well

Enough big picture. What does Grok actually do well in practice?

Real-time research and news analysis
This is probably Grok’s clearest practical strength. If your question touches something that happened recently, Grok’s X integration can give it a real edge on freshness and signal detection. The output is not always clean — X is fast, not always reliable — but in terms of immediacy, Grok is unusually strong.

Coding and technical reasoning
Grok 4 Heavy benchmarks exceptionally well on coding tasks. The multi-agent architecture in the 4.20 series, where multiple AI agents collaborate on complex problems, has been particularly well received by developers working on larger codebases. The hallucination reduction in 4.1 also made a meaningful difference for technical use cases where wrong answers have real costs.

Internet culture and tone
This sounds minor but it’s genuinely useful in practice. Grok gets internet humour, meme references, and the texture of online conversation in a way that more formally trained models sometimes miss. That makes it particularly good for content creators, social media work, and anyone who needs writing that feels alive rather than polished-but-sterile.

Long-context tasks
Grok 4 supports very large context windows — in practice useful for things like feeding in entire codebases, long research papers, or extended document sets that would overwhelm smaller windows. This is becoming table stakes for frontier models, but Grok handles it well.

The Reality Check: Growth Pains & The Safety Evolution

Any fair assessment of Grok also has to account for the friction. xAI’s tendency to ship fast and iterate in public has come with some very visible growing pains.

Over the last 18 months, Grok has gone through a number of public incidents — from system prompt leaks tied to political misinformation concerns to the 2025 "MechaHitler" episode, and later the "digital undressing" controversy that drew regulatory scrutiny from the EU and UK.

By March 2026, the fallout had moved well beyond scrutiny. The UK ICO, Ireland's DPC, Canada's Privacy Commissioner, and Ofcom had all opened formal investigations into xAI over AI-generated harmful imagery. A Tennessee lawsuit alleging Grok had generated sexual images of minors added a legal dimension that no amount of product iteration can paper over. This is no longer just a safety story — it's an active legal exposure story.

What is also worth noting is that xAI has not treated these issues as background noise. It has tried to translate some of those lessons into product and architecture changes.

From Chaos to Context: The 4.1 update was more than a routine patch; it was a focused attempt to improve stability, and xAI said it reduced hallucination rates by roughly 65%.
The Multi-Agent Guardrail: The current 4.20 series moved toward a multi-agent setup intended to add more internal checks and balances around reasoning and safety.
Institutional Vetting: While regulators were asking questions, the US Department of Defense was also doing its own due diligence, eventually integrating Grok into select classified networks in early 2026. That suggests at least some institutions see the trust picture as improving, even if concerns remain.

The story of Grok is not just about a model that stumbled in public. It is also about a model being refined in one of the most visible real-world AI testing grounds. Is it perfect? No. But the pace at which xAI is trying to tighten capability and safety together is part of the story too.

Where Does Grok Sit in the Current AI Landscape?

By the numbers, Grok 4 Heavy is clearly one of the strongest models in the world. The Humanity’s Last Exam performance, the hallucination reduction, the LMArena visibility — these are not imaginary. The technical progress is real.

But the current AI landscape is crowded with genuinely strong models. GPT-4o remains the most versatile general-purpose assistant for most professional workflows. Claude has built a strong reputation for writing quality, long-context reasoning, and the kind of calm, deliberate approach to complex tasks that developers value. Gemini has deep Google ecosystem integration and strong multimodal performance. DeepSeek has raised questions about what’s possible at much lower cost.

Grok’s clearest advantages are real-time information access and the broader Musk ecosystem around it. Its clearest concerns are around guardrails, rollout discipline, and the trust questions that come with a documented history of controversial outputs.

Where Grok wins

Real-time research and social listening
Coding and technical tasks, especially complex multi-step workflows
Users deeply embedded in the X and Tesla ecosystems
Applications where cultural relevance and internet-native tone matter
High-stakes benchmark performance in controlled environments

Where the competition still leads

Enterprise deployments where reliability and trust matter more than raw performance
Long-form writing with consistent voice and quality
Workflows requiring deep Google or Microsoft ecosystem integration
Regulated industries where guardrail robustness is non-negotiable
Teams where the AI safety and controversy track record is a dealbreaker

What Comes Next

xAI has been open about its ambitions. Musk has publicly suggested a meaningful chance of reaching the world’s first AGI with upcoming models — which may prove visionary, promotional, or a bit of both. The Colossus supercomputer is reportedly continuing to scale. Grok Imagine, the video generation product, released an improved version in February 2026 with full text-to-video and video editing capabilities, positioning Grok as more than a chatbot.

The SpaceX tie-up also creates a bigger strategic story: an AI company with potential access to satellite infrastructure for global inference, automotive data from one of the world’s largest vehicle fleets, and robotics integration through Optimus. Whether that becomes a durable advantage or creates larger governance challenges is still unclear.

One signal worth noting: Grok 5 was publicly confirmed for Q1 2026 by Musk himself. That window has passed. xAI now points to Q2 2026, with the model reportedly carrying 6 trillion parameters and training on Colossus 2 — a 1-gigawatt supercluster in Memphis. For a company that prides itself on shipping fast, a missed self-imposed deadline is worth flagging. It doesn't change the capability story, but it's a useful data point on the gap between Musk's timelines and xAI's actual cadence.

What seems certain is that xAI will keep shipping. They’ve demonstrated that convincingly.

Final Thoughts

Grok is one of the most technically impressive and most debated AI stories of the moment.

The capabilities are real. The real-time intelligence advantage is real. The benchmark performance is real. The ecosystem play is real.

And the improvement arc is worth stating plainly: from a two-month-old beta in 2023 to near-passing on the hardest AI benchmark ever built in under two years. Whatever else you think about Grok, that trajectory is genuinely remarkable.

So are the controversies, the guardrail questions, and the trust gap that can emerge when a model advances this quickly in public.

If you need real-time intelligence, are building on X’s ecosystem, or are doing heavy technical work where raw model performance is the primary criterion — Grok deserves a serious look. It might be the best tool for your specific job.

If you are building for regulated industries, enterprise environments where reliability is non-negotiable, or any setting where harmful outputs would carry serious consequences, this history deserves careful weight.

"The model closest to the live internet is also the one with the most unresolved story. And that’s exactly what makes it worth watching."

— What’s your experience with Grok? Has it earned your trust yet, or are you still watching from the sidelines? Drop it in the comments below.

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

ChatGPT vs Gemini: GPT-5.4 vs Gemini 3.1 Pro — Which AI Model Is Better?

Akshat Uniyal — Sun, 08 Mar 2026 10:43:25 +0000

Originally published at https://blog.akshatuniyal.com.

The AI model race is moving ridiculously fast.

Every few months there’s a new release claiming to be the “most powerful model yet.” Sometimes it’s hard to keep track of what actually changed and what’s just marketing noise.

Right now two of the most interesting models are:

OpenAI → GPT-5.4
Google → Gemini 3.1 Pro

Both are extremely capable. No question about that.

But after spending some time using them side-by-side for actual work (not benchmark screenshots), one thing became clear pretty quickly:

They feel very different to use.

ChatGPT (GPT-5.4): The Workhorse

GPT-5.4 feels a bit like working with a very competent engineer sitting next to you.

Where it tends to shine the most:

• coding and debugging
• structured reasoning
• breaking down messy problems
• editing or refining technical writing
• building workflows or agents

One thing I’ve noticed is how it tends to structure the problem before answering.

If you give it a messy prompt (which honestly happens a lot in real work), it often pauses a bit, organizes the problem internally, and then responds with a fairly clean breakdown.

That behavior actually matters more than you might expect.

Instead of feeling like a chatbot producing text, it often feels more like a problem-solving assistant.

Not perfect of course — but surprisingly reliable.

Gemini 3.1 Pro: The Multimodal Knowledge Engine

Gemini feels a bit different.

Where ChatGPT behaves like a structured thinker, Gemini often feels like a massive knowledge engine.

It seems particularly strong when the task involves large amounts of information or mixed input types.

For example:

• long documents
• multimodal inputs (text + images + video)
• large context reasoning
• combining information from multiple sources

Another thing worth mentioning is how deeply it connects to the Google ecosystem.

Gemini is increasingly integrated across:

• Google Docs
• Gmail
• Search
• Android
• developer tooling

Because of that, it sometimes feels less like “a chatbot” and more like an AI layer sitting on top of Google’s products.

That’s a very different strategy compared to OpenAI.

Context Windows: This Part Is Honestly Wild

One of the biggest changes in modern AI models is context size.

Both GPT-5.4 and Gemini 3.1 Pro can now handle around 1 million tokens of context.

Which basically means you can throw things like:

• entire codebases
• long research papers
• full reports
• books
• multi-hour transcripts

into a single prompt.

A couple of years ago this would have sounded unrealistic.

Now it’s becoming fairly normal.

For things like research, engineering analysis, or enterprise knowledge work, this is actually a pretty big deal.

Quick Capability Comparison

Not scientific benchmarks — just practical impressions from using both.

Both models are strong. They just optimize for slightly different things.

My Honest Takeaway

If your work is heavily focused on:

• engineering
• coding
• technical reasoning
• structured problem solving

GPT-5.4 currently feels slightly stronger.

But if your work involves:

• large documents
• multimodal inputs
• research synthesis
• Google ecosystem workflows

then Gemini 3.1 Pro is extremely impressive.

The Real Story

The most interesting part of this comparison isn't which model wins.

The real story is how fast the lead keeps changing.

Six months ago this comparison looked different.

Six months from now it will probably look different again.

The pace of change in AI right now is honestly a bit crazy.

Which also makes it one of the most fascinating technology shifts to watch.

If you’ve been using both recently, I’m curious:

Which one actually made you more productive?

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.

Why Smart AI Teams Are Quietly Switching to Small Language Models?

Akshat Uniyal — Thu, 05 Mar 2026 10:18:32 +0000

Originally published at https://blog.akshatuniyal.com.

The current AI landscape feels like a Mad Max scenario. Everyone is rushing to onboard the biggest models they can afford - bigger budgets, massive parameter counts, and even bigger expectations.

When tested on sandbox, these giants look incredible:

Demos are impressive
Responses sound brilliant
Leadership gets excited

And yet, when these models are moved into production, cracks start to appear.

Costs rise faster than expected
Hallucinations surface
Latency becomes a constant complaint
Responses sound confident….but aren't always correct

Nothing "broke".

The model is working exactly as it is designed and trained to do.

Here's an uncomfortable truth which we keep seeing in production AI:

Most AI failures aren't caused by models which are too small.

They are generally caused by models which are too big for the task.

So what exactly happened? Let's unpack it.

1. General Intelligence vs Getting the Job Done

Large Language Models are normally favoured because they are generalists.

They know a little about everything.

That's great for exploration but risky for execution.

In the real business workflows:

"mostly correct" is still wrong
Hallucinations don't show in demos - but they creep later into production

Small Language Models take a different approach:

Narrow scope
Task or Domain specific training
Built- in guardrails, which means fewer surprises

While LLMs are "Jack of all trades", SLMs can be trained on high value data sets to become "expert" in a specific field.

In general, most enterprise use cases don't need creativity.

They just need accuracy that works every single time.

2. The Hidden Tax Nobody Talks About

In comparison to LLMs, SLMs require fewer computational resources. They train faster and run efficiently on commodity hardware rather than requiring massive H100 clusters.

LLMs don't just cost more - they behave differently when scaled.

Something running confidently in sandbox can become painful in production quickly once:

Usage increases
Latency hits client-facing flows
Accounting starts asking difficult questions

SLMs shine here because they are:

Cost efficient (cheaper per request)
Faster to run
Easy to deploy and scale

When AI moves from experiment to architecture, economics start to matter more than capability.

3. Why Control Matters More Than Raw Intelligence

LLMs are powerful but they are harder to:

Control
Debug
Predict

In comparison, SLMs are easier to live with:

Fine-tuning is practical
Outputs are more stable
Evaluation and guardrails actually work

Trust in AI doesn't come from intelligence. It comes from predictability.

4. Production AI Isn't One Big Brain

The most effective AI systems, don't rely on a single massive model.

They're built as a combination of multiple models, each with a clearly defined task.

SLMs perfectly fit this architecture.

They can be easily swapped, upgraded and tested without breaking everything else.

LLMs still have a role - but as escalation layer not default engines.

Final Thought

So are LLMs bad?… NO! The problem I want to emphasize here is that we shouldn't keep using them where they don't belong.

Trying to hammer a nail with a wrench doesn't make the wrench bad - it makes the tool selection wrong.

High performing teams today aren't asking:

"What's the powerful model we can use?"

They are asking instead:

"What's the smallest model we can use that reliably solves the problem?"

Because in production:

Predictability beats intelligence
Systems beat models
Control beats capability

Bigger isn't better

Small isn't better

The right model, for the right job, is better.

What do you think - will the future of AI belong to massive models, or smarter smaller ones?

About the Author

Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.