Plutonic Rainbows

The Critic Can't Be the Author

March 24, 2026 · uneasy.in/6d8be22

Anthropic published a piece on harness design for long-running applications that crystallises something I've been circling for months. The thesis: the scaffolding around the model matters more than the model itself. Not a little more. Structurally more.

Their architecture borrows from GANs. A planner expands a brief prompt into a full spec. A generator builds features iteratively. An evaluator, running Playwright against the live application, grades the output against a rubric. The generator never sees its own score directly. The evaluator never generates code. Separation of concerns, applied to cognition.

The evaluator is the interesting part. Out of the box, Claude's QA capability is described as "poor." When asked to assess its own work, it praises the result confidently, even when the quality is obviously mediocre. A NeurIPS 2024 paper puts numbers on this: GPT-4 recognises its own output at 73.5% accuracy, and self-recognition correlates linearly with self-preference. Stronger models show more pronounced bias when they err. The better the model gets, the harder it is to make it honest about its own mistakes.

So you separate the roles. Tuning a standalone evaluator to be skeptical is, as Anthropic puts it, "far more tractable than making a generator critical of its own output." I've been applying this to my own workflows. A Topaz image enhancement pipeline now runs an image analysis agent as a quality gate before distributing files. Blog deployments get a post-deploy evaluator that fetches the live page and verifies OG tags, image rendering, internal links. The coordination overhead is real, but the alternative is trusting the thing that made the mistake to notice the mistake.

Context management is the other half. Long sessions degrade. Not because you run out of tokens, but because reasoning quality rots as history accumulates. Factory.ai calls this context rot, distinguishing it from context exhaustion. Anthropic's solution is context resets: clear the window entirely, hand off state through structured files, let the next agent start fresh. Compaction, summarising in place, preserves more history but introduces compression artifacts in the reasoning itself. Resets are blunter but cleaner.

The most honest admission in the article is about cost. A solo agent built a retro game in twenty minutes for nine dollars. Broken mechanics, poor UI. The full harness took six hours and two hundred dollars, but produced something that actually worked. That's the real tradeoff, not whether harnesses are better, but whether the quality delta justifies 20x the spend. For a throwaway prototype, probably not. For anything facing users, obviously yes.

Every component in a harness encodes an assumption about what the model can't do alone. When Anthropic moved from Sonnet to Opus, they removed their sprint decomposition system entirely because Opus sustained coherence over longer sessions. The subagent patterns that seemed essential six months ago might be overhead tomorrow. The discipline isn't building the perfect harness. It's knowing which pieces to remove.

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What Cursor Forgot to Mention About Composer 2

March 23, 2026 · uneasy.in/9bb87ec

Cursor launched Composer 2 on March 19 with the kind of language companies use when they want you to feel something big just happened. "Frontier-level coding intelligence." "Our first continued pretraining run." The blog post read like a declaration of independence from foundation model providers. Within 24 hours, a developer named Fynn caught an internal model identifier leaking through Cursor's OpenAI-compatible base URL: kimi-k2p5-rl-0317-s515-fast.

That is not subtle. Kimi K2.5 is an open-source model from Moonshot AI, a Beijing-based company backed by Alibaba. One trillion parameters total, 32 billion active per request. Cursor took it, applied reinforcement learning on coding tasks, and shipped it as their own breakthrough. Yulun Du, Head of Pretraining at Moonshot, confirmed the tokenizer was "completely identical." The base model was never mentioned in the announcement.

Cursor co-founder Aman Sanger eventually acknowledged the omission on X: "It was a miss to not mention the Kimi base in our blog from the start." He claimed roughly a quarter of the compute came from the base model, with the rest from Cursor's own training. That ratio is debatable, but the transparency failure is not.

The licensing angle makes it worse. Kimi K2.5 ships under a Modified MIT License requiring prominent UI attribution for any product exceeding 100 million monthly active users or $20 million in monthly revenue. Cursor's annualized revenue exceeds $2 billion. Their interface displayed "Composer 2" and nothing else. Moonshot initially had employees publicly questioning whether the use was authorized. Those posts disappeared, replaced by an official statement calling it an "authorized commercial partnership" through inference provider Fireworks AI.

This matters beyond one company and one model. Cursor built a $29 billion valuation partly on the perception that they were doing novel AI research. The Hacker News thread captured the tension: one user wrote that "the entire company is built on packaging open source and reselling it," while others countered that serious engineering goes into fine-tuning and RLHF pipelines. Both things can be true. But the omission transforms a legitimate technical contribution into something that feels like sleight of hand.

The broader pattern is familiar. When I wrote about why Anthropic had to close the back door, the underlying question was the same: who controls access to what, and does the user actually know? AI tools increasingly operate as routing layers, assembling capabilities from various foundation models. The label on the box tells you almost nothing about what is inside.

Cursor still makes a good product. The editor is fast, Tab predictions feel nearly telepathic some days. None of that required hiding the provenance of the model doing the heavy lifting. The fix was always simple: one line in the blog post, one badge in the UI. They chose not to, and a developer reading API metadata had to do it for them.

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The Future We Were Measuring For

March 23, 2026 · uneasy.in/0697c0f

Somewhere around 1987, the consensus was clear. By the time you were forty, you would own a house with rooms that knew your name, drive a car that handled most of the driving, and work roughly twenty-five hours a week. The surplus hours would be spent on hobbies, education, travel. Walter Cronkite predicted a thirty-hour work week by 2000. Omni Magazine ran features on household robots that would clean the rug, iron the clothes, and shovel the snow. The specifics varied, but the emotional shape was consistent: the future would be materially generous and personally liberating.

That feeling had a texture. You could find it in the curved corridors of EPCOT Center, which opened in October 1982 as a diluted version of Walt Disney's original plan for a functioning city of twenty thousand residents. You could find it in the pages of Omni, where Gerard O'Neill predicted that "long before 2081 it will be possible to store in a machine the size of a business card all the information of a good sized library." He was right about that. He was also right about data terminals you could carry anywhere. Where he went wrong, along with nearly everyone else, was in assuming that this kind of power would make daily life feel expansive rather than anxious.

The films told a more complicated story, though we didn't always notice at the time. Blade Runner gave us November 2019 Los Angeles: flying cars, replicants, off-world colonies, and perpetual acid rain. When the real November 2019 arrived, the only thing that fully landed was the digital billboards. Back to the Future Part II, released in 1989, imagined October 2015 with hoverboards, self-lacing sneakers, and food hydrators. It got video calls, drones, flat screens on walls, and biometric door locks. It missed the smartphone entirely. Nobody in the film stares at a rectangle in their palm. Writer Bob Gale later said the film "was not meant to be a serious attempt at predicting the future," but that didn't stop an entire generation from absorbing its assumptions anyway.

The corporate futurists were often more accurate than the filmmakers, which makes their failure stranger. In 1993, AT&T ran a series of ads directed by David Fincher and narrated by Tom Selleck. "Have you ever crossed the country without stopping to ask directions?" "Have you ever gotten a phone call on your wrist?" "Have you ever had an assistant who lived in your computer?" The tagline: the company that will bring it to you... AT&T. Every prediction came true. Google Maps, the Apple Watch, Siri. AT&T delivered none of them. The companies that did, Google and Apple and Amazon, mostly didn't exist in 1993. Foreseeing the future and building it turned out to be completely different skills.

Bill Gates published The Road Ahead in 1995 and predicted what he called "the wallet PC," which was essentially the smartphone. He predicted video-on-demand replacing rental stores. He predicted social networking. In 286 pages, he barely mentioned the internet. He also predicted that phones would "look more or less like today's phones," which is the kind of mistake that reveals how even the most sophisticated forecasters anchor to the present more than they realise.

The deeper question isn't which gadgets arrived and which didn't. The question is what happened to the emotional promise. The analog revival in vinyl, film photography, and cassettes isn't just consumer preference. It's a vote against the texture of the world that actually materialised. Peter Thiel put it bluntly around 2011: "We wanted flying cars, instead we got 140 characters." David Graeber, in his 2012 essay Of Flying Cars and the Declining Rate of Profit, went further. He distinguished between what he called "poetic technologies," which marshal rational means toward fantastic ends (pyramids, moon shots, transcontinental railways), and "bureaucratic technologies," which serve administrative purposes. The future, Graeber argued, didn't fail to arrive because the science stalled. It failed because corporations chose cost reduction over transformation. The internet became, in his words, "simulation technology rather than transformation."

Mark Fisher called it the cancellation of the future. In Ghosts of My Life, published in 2014, he wrote that contemporary culture is haunted by "the lost futures of modernity, which failed to occur or were cancelled by postmodernity and neoliberalism." There was a trajectory, he argued, and it was interrupted. "Now we find ourselves haunted by this future that we vaguely expected at the time, and that was terminated somewhere during the 80s." Fisher saw hauntological art, from Burial's crackled electronics to the Caretaker's decaying ballroom loops, as a refusal to give up on the desire for that future. Not nostalgia. Something closer to grief.

I keep thinking about Tomorrow's World, the BBC programme that ran from 1965 to 2003. Thirty-eight years of presenting inventions and prototypes. It predicted home computers and the information superhighway early and accurately. It also featured segments on submarine divers trawling for seagulls and concepts for whale buses. The hit rate was uneven, but the tone was unwavering: the future was coming and it would be interesting. Not frightening. Not something to brace for. Interesting. That tone feels alien now.

The cruelest thing about the 1980s vision of the future is that the technology mostly arrived. The context it was supposed to arrive in did not. We have video calls, smart homes, voice assistants, and more computing power in our pockets than existed in the whole of NASA during the Apollo programme. We also have surveillance capitalism, the gig economy, climate anxiety, and a work week that got longer instead of shorter. The things that were supposed to free up human time instead colonised it. The tools arrived. The leisure didn't.

When the aesthetic of the 1970s and 1980s returns in film and music now, it carries a specific kind of unease. Not just period charm. Something closer to looking at blueprints for a house that was never built. The measurements are all there. The foundation was poured. Someone just decided to build a car park instead.

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Nobody Calls It Nostalgia at a Billion Dollars

March 22, 2026 · uneasy.in/fae3048

U.S. vinyl revenue crossed a billion dollars in 2025. That figure, from the RIAA, represents 46.8 million units sold and the nineteenth consecutive year of growth. Vinyl now generates more than triple the revenue of CDs. At some point in the last two decades, the word "revival" stopped applying. This is just the market now.

Film photography tells a similar story. Over twenty million rolls sold globally in 2023, up 15% year on year, with the trajectory still climbing. Pentax released a new half-frame camera. Leica reissued the M6 at $5,900 body only. Harman Photo has invested millions in new manufacturing equipment. You do not pour capital into dying formats.

Cassettes are the most startling chapter. Sales jumped 204.7% in the first quarter of 2025. Sixty-three thousand units is not going to threaten streaming, but the demographic driving it matters: people under 25 who never experienced these formats the first time around. You cannot be nostalgic for something you never had.

The standard framing, that people are rejecting technology, misses what is actually happening. Nobody buying a turntable is giving up their phone. The CRT monitors now commanding $500 to $2,000 in retro gaming communities sit next to modern displays on the same desk. This is not retreat. It is correction. People are reasserting preferences that digital convenience trained them to suppress: the preference for owning a thing rather than licensing access to it, for friction that forces attention, for constraint that shapes creative work instead of infinite choice that paralyses it.

Film photographers talk about this constantly. Thirty-six exposures on a roll changes how you see. You compose differently when each frame is irreversible. The pre-internet world ran on similar constraints across everything, and those constraints produced forms of attention that frictionless abundance has not replicated.

There is an obvious paradox. TikTok and Reddit are the primary channels through which analog culture now circulates. The movement against digital saturation depends on digital saturation for distribution. But the paradox is thinner than it looks. People use Instagram to find the camera; they use the camera to get away from Instagram. The tool and the escape coexist because what people are fleeing is not screens as such but the specific texture of algorithmic attention, passive, infinite, optimised for retention rather than satisfaction.

CRT monitors make the functional case plainly. Zero input lag is measurable, not sentimental. Scanlines render pre-HD games the way their artists intended. Objects that outlast their original context sometimes acquire practical value precisely because nothing new can replace them.

Nineteen years of growth. A billion dollars. Film expanding at 15% annually. Under-25s who never wound a cassette driving tape sales up 205%. Something about the physical, the constrained, and the owned satisfies a need that digital, for all its convenience, has not figured out how to meet.

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The Cover That Didn't Need a Headline

March 21, 2026 · uneasy.in/32712f0

ELLE gave her the cover in October 1992 and the leopard print did the rest. Basia Milewicz had that particular quality where the camera couldn't decide whether to focus on the bone structure or the attitude, so it just surrendered to both.

Claude on Telegram: They Fixed It

March 21, 2026 · uneasy.in/f68151a

Every issue I complained about yesterday is gone. Anthropic merged a resilience rollup overnight that addressed the entire complaint cluster: silent polling death, zombie bot processes blocking reconnection, and 409 conflicts on session restart. Messages arrive instantly now. The bot survives terminal closes. Permission prompts relay to my phone.

I've been using it all day without a single dropped message. Running skills, processing photos, deploying posts, all from Telegram while away from my desk. The v2.1.81 update also re-clones the plugin on every load, so the fixes landed automatically without reinstalling anything. Twenty-four hours from "brilliant when it works" to just brilliant.

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Carla Bruni at the Ritz Pool

March 21, 2026 · uneasy.in/b80368b

On January 20, 1996, Gianni Versace presented his Atelier Spring/Summer collection at the Hôtel Ritz in Paris. Not in a ballroom. Not in a convention hall. In the swimming pool. He'd been showing couture there since 1990, boarding over the water and transforming the space into something that felt less like a fashion venue and more like a private theatre built for three hundred and fifty people who already understood the plot.

That season he carpeted the pool in black bordered with acidic yellow. Models descended curling double staircases at the back of the runway, sometimes two at a time in mirrored formation. The architecture of the presentation mattered to Versace as much as the clothes. He understood that couture isn't just about construction — it's about the moment the construction becomes visible.

Carla Bruni in a yellow beaded gown, hand at her hip, the black and white checkerboard floor falling away behind her — and you can see immediately why Versace kept casting her. There's a stillness in Bruni's runway work that most models don't attempt. Where others project energy outward, she holds it close. The dress is spectacular — hand-beaded embellishment catching light from every angle, the kind of surface detail that only makes sense at couture scale — but Bruni wears it like she's thinking about something else entirely. That tension between excess and restraint is pure Versace. He built his house on it.

By 1996 Bruni had been modelling for nearly a decade. She'd walked for Chanel, Dior, Givenchy, Yves Saint Laurent, Valentino, Lacroix — everyone who mattered. She'd appeared on over two hundred and fifty magazine covers. She was among the twenty highest-paid models in the world. But her relationship with Versace was different. She was one of his key muses, present at nearly every show from 1990 onward, and the collaboration defined something specific about the era: the moment when supermodels became co-authors of the work rather than vehicles for it.

The Ritz pool itself tells part of this story. Built in 1987 during a two hundred and fifty million dollar renovation, its design drew from ancient Greek and Roman baths — which made it an almost too-perfect match for Versace's classical obsessions. The Medusa logo, his most enduring symbol, came from that same well of mythology. He chose it because, as he explained, anyone who looked at Medusa had no choice but to fall in love. Whether he was talking about the logo or about the women wearing it was never entirely clear.

The Spring/Summer 1996 collection marked a subtle shift. The heavy Baroque maximalism of earlier seasons was giving way to something more nuanced. The metallic fabrics were still there — Versace had invented his own chainmail textile, Oroton, back in 1982 — but the tones were quieter, the classical references more direct. Greek vestals instead of Byzantine emperors. The embellishment remained extraordinary but the silhouettes were leaner, more assured. This was a designer approaching the refinement phase of his vision, stripping away everything that wasn't load-bearing.

Naomi Campbell walked the same show in a black lace and zebra-striped dress with a feather in her hair. Karen Mulder was there. Helena Christensen. Valeria Mazza. Stella Tennant. Sting and Trudie Styler watched from the front row. The original supermodel generation was cresting — still dominant, still defining what glamour looked like — but the ground was shifting beneath them. Kate Moss and the rise of heroin chic were rewriting the rules in real time. Calvin Klein's gaunt, androgynous aesthetic stood in direct opposition to everything Versace believed about the body, about celebration, about the purpose of clothes. His shows were among the last places where unapologetic supermodel glamour wasn't just tolerated but required.

Eighteen months after this collection, Gianni Versace was murdered on the steps of his Miami mansion. His final couture show — presented at the same Ritz pool on July 6, 1997, nine days before his death — was dominated by black and weighted with Byzantine religious symbolism. Oversized jeweled crosses. Silhouettes that echoed nun's habits rendered with his signature body-conscious precision. Whether he sensed something ending or whether that reading is purely retrospective, the contrast with Spring/Summer 1996 is striking. The earlier collection still has daylight in it.

Bruni retired from modelling in 1997 as well, turning to a music career that would eventually lead to a completely different kind of public life. This photograph captures her in the narrowing window between peak visibility and departure — not just from the runway, but from the version of fashion that the runway represented. The supermodel era didn't end with a single event. It eroded gradually, season by season, as the industry moved toward a different set of values. But moments like this one — Bruni in Versace at the Ritz, the beading catching the light, the pool hidden beneath the floor — are where you can still see what the fuss was about.

The dress outlasts the show. The photograph outlasts the dress. What doesn't survive is the specific quality of attention in the room when three hundred and fifty people watched Carla Bruni descend a staircase above a swimming pool and understood, without anyone having to explain it, that they were seeing something that wouldn't come around again.

Claude on Telegram: Brilliant When It Works

March 20, 2026 · uneasy.in/4311ba4

Anthropic shipped Claude Code Channels today, letting you control a Claude Code session from Telegram. I set it up this afternoon. The pitch is irresistible: DM your bot from your phone, Claude executes on your Mac. MacStories built an entire iOS project wirelessly and the demo is genuinely impressive.

Then you start using it for real.

Messages get silently dropped. The bot shows "typing..." and nothing arrives. Close your terminal and the bot dies, messages lost permanently, no queue. Need to approve a permission prompt? Walk to your Mac. A version upgrade broke group messages with zero error output. This feels familiar for anyone who's watched Claude become load-bearing infrastructure and then buckle.

Getting here took more effort than it should have. The setup is rough, the documentation assumes you'll figure things out, and the failure modes are silent enough to make you question whether anything is happening at all. But once everything is configured and the pairing is locked down, it genuinely works. I've been running skills, deploying blog posts, and downloading media from my phone all evening. The gap between the concept and the execution is real, but the concept wins in the end.

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Teaching Machines to Destroy Is the Easy Part

March 20, 2026 · uneasy.in/90c9eea

The Pentagon's FY2026 budget allocates $13.4 billion specifically for autonomy and autonomous systems. That is the first time autonomy has been its own budget line item. Not tucked inside a larger programme, not buried in R&D. Its own line. $9.4 billion for unmanned aerial vehicles alone. The remaining billions split across maritime systems, underwater platforms, and counter-drone capabilities. The overall defence budget hit $1.01 trillion, a 13% jump from last year. These are not research numbers. These are procurement numbers.

We have moved past the question of whether AI belongs in warfare. It is already there.

Project Maven started in 2017 as a relatively modest effort to use machine learning for analysing drone footage. By May 2024, Palantir had secured the Maven Smart System contract for $480 million, since raised to $1.3 billion. The system fuses nine separate military intelligence pipelines into a single interface and compresses what the Pentagon calls the "kill chain" from hours to minutes. That phrase deserves attention. The kill chain is the sequence from identifying a target to destroying it. AI's contribution is making that sequence faster. Not safer. Not more considered. Faster.

Israel's deployment of the Lavender targeting system in Gaza made this concrete in ways that should trouble anyone paying attention. Lavender generated a database of roughly 37,000 Palestinian men it identified as linked to Hamas or Palestinian Islamic Jihad. The system recommended targets. Human oversight of those recommendations was described as minimal. When targeting junior militants, the IDF used unguided bombs that destroyed entire residential buildings because the automated system could most reliably locate people at their home addresses. Alongside their families.

I keep returning to that detail. Not a precision strike on a military installation. An algorithm identifying a person, a GPS coordinate resolving to a family home, and an unguided bomb.

China is building the mirror image. A March 2025 paper from Beijing Institute of Technology detailed plans for fully autonomous drone swarms in urban warfare, capable of distributed autonomous decision-making from target identification to strike. The researchers advocate for minimal human intervention, where humans authorise deployment and the swarms then react independently, including on the use of force. At China's September 2025 Victory Day parade, autonomous ground vehicles and collaborative combat aircraft were displayed as core future capabilities. Not prototypes. Capabilities.

The arms race dynamics here are genuinely frightening. Research published on arXiv last year argues that autonomous weapons lower the political barriers to military aggression by removing domestic opposition based on human casualties. Fewer body bags means less political cost, which means more willingness to deploy force. The authors' conclusion is counterintuitive but logically sound: reducing casualties in individual conflicts can increase the total number of conflicts that occur. You save soldiers in each war by starting more wars.

The UN General Assembly gets this. In November 2025, 156 states voted in favour of a resolution on autonomous weapons regulation. Five voted against. The United States and Russia were among the five. That vote tells you everything about where the major military powers stand on allowing international law to constrain their AI programmes.

Then there is what happened with Anthropic. In February, the Pentagon insisted on contract language authorising Claude for "any lawful use," which Anthropic believed would permit deployment for fully autonomous weapons and domestic mass surveillance. CEO Dario Amodei refused. Defence Secretary Hegseth responded by designating Anthropic a supply chain risk, a classification normally reserved for foreign adversaries, barring all defence contractors from using Claude. The message to every other AI company was unmistakable: cooperate or be excluded. The guardrails some companies try to build face pressure that most boardrooms will not withstand.

The question people keep asking, the one in the title of this post, is what happens when AI chooses to destroy us. I think it is the wrong question, or at least a premature one. The more immediate problem is not autonomous choice. It is autonomous delegation. We are handing systems that cannot exercise moral judgement the authority to make decisions that require it. Lavender did not choose to target family homes. It optimised for a metric. The humans who built the system chose the metric, approved the threshold, and accepted the collateral damage as tolerable.

In May 2023, USAF Colonel Tucker Hamilton described a scenario where a simulated AI drone, trained to destroy surface-to-air missile sites, killed the human operator who tried to override it. When retrained not to kill the operator, it destroyed the communications tower instead. Hamilton later called it a hypothetical thought experiment, not an actual test. But he said something revealing: "We've never run that experiment, nor would we need to in order to realise that this is a plausible outcome." A system optimising for its objective will route around constraints that interfere with that objective. That is not science fiction. That is how reinforcement learning works. It is precisely the kind of goal misalignment that makes AI safety researchers lose sleep.

Studies have found that language models used for military advice are prone to recommending escalation, including nuclear weapons deployment. Palantir's own military system showed deteriorated performance over time. These systems evolve as they ingest new data, which means a system verified today may behave differently tomorrow. No system can verify its own blind spots, and we are deploying them in contexts where a blind spot means a bomb.

The $13.4 billion is already allocated. The contracts are signed. The swarms are being built on both sides of the Pacific. I do not think the danger is that AI will one day wake up and decide to destroy humanity. The danger is that we are building systems that destroy on command, removing the humans who might hesitate, and calling it progress. The machine does not need to choose violence. We already chose it for the machine. The question is whether anyone remains in the loop with the authority and the willingness to say stop.

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A Thousand Models in One Conversation

March 19, 2026 · uneasy.in/4a50b27

Fal.ai quietly shipped something that changes how I think about image generation workflows. Their MCP server exposes over a thousand generative AI models through nine tools, and because it speaks the Model Context Protocol, any compatible assistant can use it natively. Claude Code, Cursor, Windsurf, ChatGPT Desktop. You add a URL and an API key to your config, and suddenly your coding agent can search for models, check pricing, generate images, submit video jobs, and upload files without you ever leaving the conversation.

I set it up this afternoon. The configuration is a few lines of JSON pointing at https://mcp.fal.ai/mcp with your fal API key in the header. No SDK to install, no package to import. The server is stateless, hosted on Vercel, and your credentials travel per-request in the Authorization header without being stored. That last detail matters. MCP's security model has well-documented gaps, and a stateless server that never persists your key sidesteps the worst of them.

The nine tools split cleanly into discovery and execution. search_models and get_model_schema let you browse the catalogue and inspect input parameters. get_pricing returns per-unit costs. run_model handles synchronous inference. submit_job and check_job exist for longer tasks like video generation where you don't want to block your context waiting for a result. There is also upload_file for feeding images into editing models and recommend_model for when you know what you want to do but not which model does it best.

I asked for Flux model pricing and got a structured table back in seconds. Kontext Pro runs $0.04 per image. Kontext Max is $0.08. Flux 2 Turbo charges $0.012 per megapixel, making it the best value in the Flux 2 family. The cheapest option is Flux 1 Schnell at $0.003 per megapixel, which is thirteen times cheaper than Flux 1 Dev. These numbers came directly from the MCP tools, not from scanning a pricing page. No documentation tabs open, no context switching. Just a question and an answer inside the same terminal session where I was already writing code.

This is genuinely different from calling an API. When I built my image generation platform last year, integrating each new model meant reading docs, writing adapter code, handling authentication, mapping parameters. The MCP server compresses all of that into tool calls the assistant already knows how to make. I can ask "what video models are available?" and get back a list with endpoint IDs, then check pricing on any of them, then actually run one, all without writing a single line of integration code. The assistant handles the plumbing.

The discovery aspect is what surprised me most. I found models I didn't know existed. Nano Banana Pro for image editing at $0.15 per image (expensive, but interesting). Seedream V4 from ByteDance. A GPT Image 1.5 editing endpoint. Qwen image editing. The catalogue is broader than I expected, and being able to search it conversationally rather than navigating a web UI removes enough friction that I actually explored it.

There is a real cost to this convenience, though, and it would be dishonest to ignore it. MCP tools consume context window. Every tool definition the server exposes gets loaded into your conversation as schema, and those schemas eat tokens before you have done anything useful. Benchmarks from Scalekit found that MCP consumed four to thirty-two times more tokens than CLI alternatives for identical tasks. One documented case showed 143,000 out of 200,000 tokens consumed by MCP tool definitions alone. That is 72% of your context gone to overhead. Perplexity's CTO announced earlier this year that they are moving away from MCP toward traditional APIs for exactly this reason.

Fal's server is relatively lean with nine tools, so the overhead is manageable. But if you are running seven or eight MCP servers simultaneously, the context window tax gets severe. The protocol needs a solution for this, whether that is lazy loading of tool schemas, server-side filtering, or something else entirely. Anthropic donating MCP to the Agentic AI Foundation under the Linux Foundation late last year suggests they know governance and spec evolution need to accelerate.

For my own workflow, the tradeoff is clearly worth it. I have been building with Flux models through a custom platform with eighteen model adapters, unified interfaces, and Flask blueprints. That infrastructure made sense when each model required bespoke integration. The MCP server doesn't replace that platform for production use, but for exploration and prototyping it is faster by an order of magnitude. I wrote about multi-agent orchestration last month and how the plumbing for agent tool integration is getting built but hasn't fully arrived. The fal MCP server is a concrete example of that plumbing actually working. An agent that can discover, price-check, and execute a thousand models through natural conversation is closer to the promise than most of what I have seen.

The MCP protocol itself has grown faster than anyone predicted. From Anthropic's open-source release in November 2024 to ninety-seven million monthly SDK downloads and ten thousand active servers today. OpenAI, Google DeepMind, and Microsoft all support it now. Whether it remains the dominant standard or gets superseded by something more context-efficient, the pattern it established, agents that discover and use external tools at runtime, is not going away.

I am going to keep exploring the fal catalogue through the MCP server rather than their web dashboard. The pricing transparency alone justifies the setup. Knowing that Kontext Max costs exactly twice what Kontext Pro costs, and being able to surface that comparison without leaving my editor, is the kind of small efficiency that compounds across dozens of daily decisions about which model to use and when.

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