Tony Park (@rtnyprk) header
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SF
Joined November 2025
Photo by Tony Park (@rtnyprk)Photo by Tony Park (@rtnyprk)Photo by Tony Park (@rtnyprk)Photo by Tony Park (@rtnyprk)Photo by Tony Park (@rtnyprk)Photo by Tony Park (@rtnyprk)Photo by Tony Park (@rtnyprk)Photo by Tony Park (@rtnyprk)
Introducing the Melange CLI You can use your favorite AI agents to upload any AI model, get benchmark reports, and deploy on-device. Here’s a quick demo of the cool things you can get your agent to do. Get set up: github.com/zetic-ai/melange-…
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Tony Park retweeted
Tell your agent to optimize your site’s web fonts. I’m seeing a huge difference on my blog. Full prompt: Make this site's web fonts load faster without changing how anything looks. Shrink font bytes as far as possible, keep rendering identical, and prove it with before/after pixel diffs. Treat any visual change as a regression unless I approve it. If an optimization would change rendering, tell me what it saves and what it changes, and keep the site looking the same by default. Keep the current loading behavior: don't touch font-display or fallback fonts. Don't add font processing to the build; write a script I run by hand, commit its output, and note when it needs rerunning. First measure the current state: each font file's size, glyph count, variable axes, OpenType features and hinting, plus font bytes per page and what each page preloads. Then find out what the site actually uses, checking inline component styles as well as stylesheets: which characters appear in the content, which axis values and weights each font needs (including one-off overrides and bold inside italics), which OpenType features are turned on, and CSS side effects like `hyphens: auto` using the U+2010 hyphen. Cut each font down to that. Keep the used characters plus Latin-1 and Latin Extended-A, and leave CJK to system fonts if that's how it renders today. Limit variable axes to the ranges in use, and only fix an axis at one value if nothing overrides it. Drop unused OpenType features and hinting. Split each font into two files. A small core with ASCII and the punctuation most pages use (curly quotes, dashes, ellipsis) gets preloaded. An extended file behind `unicode-range` only downloads when a page needs one of its characters. Each file's `unicode-range` should list only characters it actually contains. Watch out for a few things. A generated fallback face between the core and extended files will draw the extended characters before the real font does. Fonts with a Reserved Font Name under the OFL must be renamed internally once subset. Check the generated files, since font tools have quirks. Before calling it done, build a baseline from the same content and pixel-diff before and after on pages with code, italics, accented characters, components with their own font settings, and CJK. Explain every visible difference: content change, regression to fix, or harmless sub-pixel rounding. Re-measure bytes per page and confirm the extended files only load where needed. When you're done, tell me the bytes before and after per font and per page, the pixel-diff results, anywhere you deviated from what I asked and what it cost, and how to rerun the script. Later, it may be worth embedding the core font in the global stylesheet and precaching the extended files after idle.
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As someone who has personally spent $500k / mo+ on Google Ads for years, I can tell you with certainty: This revenue growth in Search is artificial & extremely unhealthy for Google’s business long term Search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries Google’s response? Manufacture revenue growth via short-sighted, highly extractive, customer-hostile tactics. I.e. charge advertisers more for lower quality clicks, including clicks they do not want and explicitly did not approve Google to charge them for A few examples to illustrate: For all of its history until recently, Google operated on a 2nd price auction model I.e. if you bid $5 CPC and the next highest bidder bids $1 CPC, Google charged you $1.01 for the click (one penny more than the 2nd highest bidder) rather than the $5 you bid This was a genius move by Google early on as it incentivizes advertisers to input their true maximum willingness to pay rather than trying to play the game of bidding low and constantly adjusting to try to stay just ahead of the next highest bidder while still not paying too much However recently, Google silently deprecated the 2nd price auction and began charging advertisers as much as their bid and budget caps allow, regardless of what anyone else is bidding It’s a short-sighted cash grab at the expense of the long term health of the advertiser ecosystem Making thing worse, Google also recently nerfed keyword targeting precision Google previously had precise keyword targeting settings that allowed advertisers pick individual search phrases to bid on, defined down to the character w/ exact match or phrase match targeting This was one of the core features that made search advertising magic, enabling advertisers to run extremely precise campaigns based on exactly what their target customer typed But now, even if you bid on a specific term or phrase using the strictest exact -match targeting settings, Google will show your ad across 1000’s of unrelated keywords, labeling them as as “exact match (close variant)” The definition of “close variant” means whatever they want it to and changes constantly. The result is advertisers get billed for clicks that are totally irrelevant to their business and that their targeting settings explicitly forbid Google from targeting. Google does it anyway and there’s no ability to turn this off So now exact match is broad match, and broad match is just meaningless spam This is all very bad for advertisers, but for Google, it allows them to show your ad and bill you for clicks across 1000x more searches that were previously going unmonetized (mainly because they’re garbage queries no one wants) This is how you grow revenue atop declining search volumes Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day And the extra spend is entirely on the garbage keywords Google arbitrarily throws in as “exact match (close variants)” which have no value to our business, but can’t be turned off Google offers no refunds nor any recourse for overspend or spend on keywords you explicitly did not target These are not the actions of a healthy business. These are the actions of company whose core business is in decline but desperately needs to pump quarterly earnings so Wall Street will continue to fund insane capex while hopefully looking through their rapidly deteriorating negative free cash flow Google operated a benevolent monopoly for the better part of 25 yrs Meaning the value Google captured from Search was but a small fraction of the value it created, and that spread produced a potential energy that justified expectations of high earnings growth far, far into the future This is now no longer the case At the alter of AI capex, Google is sacrificing the golden goose
Q2 was an amazing quarter, with our AI investments redefining what’s possible across every part of our business. Alphabet revenue grew 24% YoY and Google Cloud accelerated to 82% growth. We saw exciting momentum across the board from Search to YouTube to the Gemini app (which reached 950M monthly active users). Our model APIs are processing 22B tokens/min (up from 16B+ last quarter) driven by our workhorse Flash models. We’re also seeing great adoption of Gemini Enterprise, used by 90% of the Fortune 100, as well as strong demand for our security solutions. Outstanding results and momentum, and such an exciting moment—thanks to all of our partners and employees around the world! 🙌 About to hop on the call!
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I take all my meeting notes in @anarlogapp. It lets you BYOK, so I benchmarked which LLM writes the best one. Three decision models judged: Jev, Mammoth (@baselabs), Decider (@perplexitydevs). 1. Grok 4.6 2. GPT-6 Luna 3. DeepSeek V4.1 Flash 1017 4. GLM 5.3 Flash 5. Muse Spark 1.3 Contributor 6. Claude Haiku 5.5 7. Nemotron 3.5 Super VL 8. Gemini 3.8 Flash tonypark.dev/2026/10/21/whic…
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What the smartest people do on the weekend is what everyone else will do during the week in.... how many years now? what things?
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I take all my meeting notes in @anarlogapp. It lets you BYOK, so I benchmarked which LLM writes the best one. Three decision models judged: Jev, Mammoth (@baselabs), Decider (@perplexitydevs). 1. Grok 4.6 2. GPT-6 Luna 3. DeepSeek V4.1 Flash 1017 4. GLM 5.3 Flash 5. Muse Spark 1.3 Contributor 6. Claude Haiku 5.5 7. Nemotron 3.5 Super VL 8. Gemini 3.8 Flash tonypark.dev/2026/10/21/whic…
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Tony Park retweeted
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
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Check out this alternative! It's a chrome extension, and we will also open source soon to support BYOK: chromewebstore.google.com/de…
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Tony Park retweeted
I wrote an essay about why the next frontier for AI is the world outside the data center Nature is a system more complex than anything we've ever created AI can learn how the system fits together, so we can finally understand how to shape it How? naturalgeneralintelligence.a…
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turned my favorite photos into painterly ASCII mosaic images with AI, and it's pretty fun how I did it (prompts): realtonypark.github.io/2026/…
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Tony Park retweeted
Xbench: Twitter as an AI Eval It's been painfully clear over the last few days that the industry has an evals problem. We joke that x is the real eval, but what if we took that seriously? What if attention is all you need for evals too? nicodunks.github.io/xbench/
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This Meta campaign is a case study in strategic reframing, with 4 major examples: 1) REFRAMES THE AI RACE FROM “who builds it the best” TO “who distributes it to the most people”: Instead of fighting over who has the most capable models, Meta is framing the race as being won by distribution. With open models, free products, consumer dna, and 3.5 bn users, Meta is best positioned here 2) REFRAMES COMPETITORS FROM “holding the technical lead” TO “holding too much concentrated power”: Classic populist stance, with the insight that hostility to AI is largely hostility to being subjugated by an overlord class of AI perma-elites. The frontier labs’ biggest advantage (lead in capabilities) becomes a liability, while Meta’s huge user base is used to reposition Meta too powerful in its own right to being a check on the power of the frontier labs (the perfect foil) 3) REFRAMES SAFETY DEBATE FROM “more vs less safe” TO “optimists vs doomers”: OpenAI and Anthropic have spent years focusing on AI risks, and many people see this as noble and responsible. By equating safety focus to doomerism, Meta repositions itself from being less safe to being more optimistic about the future, while making competitors look like tiresome hypocrites 4) REFRAMING META’S PORTFOLIO FROM “unfocused and capricious” TO “a future for everyone”: In jumping between crypto, metaverse, AI, hardware, Meta has been accused of lack of focus and trying to do everything. But now they can position these motley initiatives as being unified by the coherent theme that Meta technology is “for everyone.” It’s not ADHD, it’s a mission to put frontier technologies into the hands of ordinary people In the process, they’re also tapping into many parts of the zeitgeist: distrust of concentrated power, support for open models, permanent underclass anxiety, doomer fatigue But Meta’s key strategic move here is to change the criteria for the AI race. Under the old criteria (capabilities of frontier models) Meta was seen as behind. Under these new criteria (widespread universal distribution of technology), Meta might actually have the strongest position in the industry
I believe everyone should have access to superintelligence, and I wrote a long piece about Meta's philosophy and values for building a positive future for everyone. meta.com/thefutureisforevery…
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The Muse Spark 1.2 API pricing is quite crazy, especially as a daily driver for most coding tasks. It's up to 250x cheaper than Fable and 150x cheaper than GPT-5.6 Sol! Price per 1M tokens: Cached input - $0.002 Input - $0.10 Output - $0.20 Try it out! dev.meta.ai/docs/quickstart
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Tony Park retweeted
Nvidia is the leading open source AI company by a wide margin. The silliness of this post is compounded by the fact that the letter did not suggest that closed source AI companies should open source their models.
I’m so excited that @JensenHuang is a believer in open source now, looking forward to the CUDA and GPU driver open source release!
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Tony Park retweeted
Try Melange: melange.zetic.ai
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Tony Park retweeted
what if a letter could hide inside sound? built this yesterday: you write a letter, it becomes a hidden, poetic message made of soundwaves. used my own voice, cloned with @GradiumAI. img2threejs by @NickDevFE. inspired by Jen Cantwell's "Letter Home."
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Dear algorithm, Please show this to people who want one place for: - Shipping on-device AI in an hour - Unlocking any chip - Unlocking any model - A production-ready 3-line SDK - Automated hardware optimization - Benchmarking 200+ real devices - Zero cloud GPU costs - 100% data privacy & offline access Thanks, The Best Platform to Build On-Device AI
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