Search

123 results for “fasse”

1. 

   Dev Community @[email protected]

   2026-10-25

   Navigating a restaurant menu can feel overwhelming—especially when you’re balancing taste preferences, dietary goals, and nutritional concerns. With the explosion of AI food tech in recent years, a new era of “smart menu” experiences is emerging. AI menu analysis isn’t just about digitizing paper menus; it’s fundamentally transforming how diners discover, understand, and choose their meals. From estimating hidden calories to surfacing dishes tailored to your preferences, these innovations are reshaping both dining out and online ordering.
   

   The Challenge: Decoding Restaurant Nutrition

   
   Most diners want to make informed choices, but restaurant nutrition information is often buried, incomplete, or absent altogether. Even when calorie counts are available, they rarely account for personal needs—like allergies, macros, or dietary restrictions. Manually scanning for options that fit your criteria can be tedious and error-prone, especially at busy or unfamiliar restaurants.

   This is where AI menu analysis comes in. By leveraging machine learning, natural language processing, and computer vision, AI-powered systems can extract, interpret, and enhance menu data at scale. The result: more transparent, personalized, and actionable restaurant nutrition insights for both diners and operators.
   

   How AI Menu Analysis Works

   1. Menu Digitization and Parsing

   
   The first step is converting static menus into machine-readable data. AI models (often using OCR—Optical Character Recognition) can scan printed or photographed menus and extract structured information. Natural language processing (NLP) is then applied to parse dish names, descriptions, and even detect ingredient lists.

   // Example: Simple menu item extraction using regex and NLP concepts
   const menuText = `
   Grilled Salmon Salad - Fresh greens, grilled salmon, avocado, and vinaigrette. 450 kcal
   Classic Burger - Beef patty, cheddar, lettuce, tomato, brioche bun. 800 kcal
   `;

   const dishRegex = /(.*?)\s*-\s*(.*?)\.\s*(\d+)\s*kcal/gi;
   const menuItems = [];
   let match;

   while ((match = dishRegex.exec(menuText)) !== null) {
   menuItems.push({
   name: match[1].trim(),
   description: match[2].trim(),
   calories: parseInt(match[3], 10)
   });
   }

   console.log(menuItems);

   Real-world systems go much further, handling inconsistent formatting, synonyms, and multilingual content.
   

   2. Nutritional Value Estimation

   
   Many menus lack detailed nutrition facts. AI can bridge these gaps by analyzing dish descriptions and predicting nutrition profiles. This typically involves:

   • Ingredient recognition: Identifying possible ingredients based on dish names and descriptions.
   • Recipe inference: Estimating likely preparation methods and quantities.
   • Database matching: Mapping extracted ingredients to known nutrient databases (like USDA or Open Food Facts).

   Machine learning models trained on vast recipe and nutrition datasets can predict calories, macros, and even allergen presence with surprising accuracy.

   // Pseudo-code: Predicting calories for a dish using AI
   async function estimateDishCalories(dishDescription: string): Promise<number> {
   // This would call an AI model or API trained on recipe/nutrition data
   const estimated = await callCaloriePredictionModel(dishDescription);
   return estimated.calories;
   }

   3. Personalization and Smart Recommendations

   
   AI menu analysis unlocks highly personalized experiences:

   • Dietary filtering: Instantly flagging dishes that fit vegan, gluten-free, or keto diets.
   • Allergen detection: Highlighting items that may contain peanuts, shellfish, or other triggers.
   • Preference learning: Recommending dishes based on past orders, ratings, or explicit preferences.
   • Health scoring: Ranking menu items by metrics like calorie density, sodium content, or “healthiness” for the user.

   Some apps leverage collaborative filtering (like Netflix or Spotify) to surface dishes that similar users have enjoyed. Others use reinforcement learning to improve suggestions over time.

   // Example: Filtering menu items for a vegetarian user
   const vegetarianKeywords = ['vegetable', 'cheese', 'tofu', 'salad', 'beans'];
   function isVegetarian(dish: { description: string }) {
   const desc = dish.description.toLowerCase();
   return vegetarianKeywords.some(keyword => desc.includes(keyword));
   }

   const vegetarianOptions = menuItems.filter(isVegetarian);
   console.log(vegetarianOptions);

   4. Enhanced Menu Interfaces

   
   Modern “smart menu” platforms use AI to power interactive interfaces, both on-premise (via tablets or QR codes) and online. Features include:

   • Dynamic search: Type “high-protein, under 500 calories” and see instant matches.
   • Visualizations: See nutrition breakdowns, allergen alerts, or compare dishes side-by-side.
   • Voice assistants: Ask for “spicy vegan options” and get spoken recommendations.

   This not only improves accessibility for all diners, but also helps restaurants stand out in a crowded market.
   

   AI Food Tech in Action

   
   A growing ecosystem of AI food tech products is making these features widely available:

   • Restaurant apps and POS systems: Many now integrate AI-driven nutrition and filtering.
   • Third-party platforms: Apps like Yummly, OpenMenu, and LeanDine offer AI-powered menu analysis, personalized recommendations, and nutrition transparency across thousands of restaurants.
   • Custom deployments: Some restaurant groups build proprietary AI models tailored to their cuisine and customer base.

   On the backend, open-source libraries (such as spaCy for NLP, or TensorFlow for custom ML models) enable developers to integrate AI menu analysis into their own applications.
   

   Challenges and Considerations

   
   While the potential is tremendous, deploying AI menu analysis at scale isn’t trivial:

   • Data quality: Menus are notoriously inconsistent—misspellings, colloquial names, and “secret sauce” ingredients can trip up even the best models.
   • Cultural and regional variation: Dishes with the same name can differ wildly in composition.
   • Privacy and consent: Personalization requires collecting user preferences and sometimes health data, raising important privacy questions.
   • Model transparency: Explaining how calorie or allergen predictions are made is essential for trust and regulatory compliance.

   Addressing these challenges requires careful engineering, ongoing model retraining, and clear communication to both restaurants and diners.
   

   Building a Simple AI-Driven Smart Menu (Example)

   
   Suppose you want to provide a “smart menu” experience in your own restaurant web app. At a high level, you’d:

   1. Digitize the menu (via OCR, manual entry, or import).
   2. Parse dish descriptions and structure the data.
   3. Augment with nutrition estimates using an AI API or open database.
   4. Add filtering and ranking based on user dietary preferences.
   5. Display results in a user-friendly, interactive UI.

   Here’s a simplified TypeScript-style sketch:

   type MenuItem = {
   name: string;
   description: string;
   calories?: number;
   isVegan?: boolean;
   };

   function filterMenu(
   items: MenuItem[],
   { maxCalories, veganOnly }: { maxCalories?: number; veganOnly?: boolean }
   ): MenuItem[] {
   return items.filter(item => {
   if (maxCalories !== undefined && (item.calories ?? Infinity) > maxCalories) {
   return false;
   }
   if (veganOnly && !item.isVegan) {
   return false;
   }
   return true;
   });
   }

   // Example usage:
   const smartMenu = filterMenu(menuItems, { maxCalories: 500, veganOnly: true });

   You could further extend this with AI-powered search, nutritional scoring, or even integration with fitness tracking apps.
   

   The Road Ahead

   
   The fusion of AI menu analysis and restaurant nutrition transparency is just beginning. As machine learning models improve and data becomes richer, expect even more granular, actionable insights—like real-time portion customization, automatic pairing suggestions, or even “health nudges” based on your goals.

   For both diners and restaurants, smart menus powered by AI food tech are making eating out (and ordering in) a more informed, inclusive, and enjoyable experience.
   

   Key Takeaways

   
   

   • AI menu analysis transforms static restaurant menus into dynamic, personalized, and nutrition-aware experiences.
   • Restaurant nutrition information is becoming more transparent and actionable through machine learning and NLP.
   • Smart menu systems enable dietary filtering, allergen detection, and personalized recommendations at scale.
   • A host of AI food tech solutions—from open-source libraries to commercial platforms—make integration possible for developers and restaurants of all sizes.
   • Challenges remain around data quality, privacy, and transparency, but the future of AI-powered dining is bright—and getting smarter with every meal.

   
   #ai #healthtech #webdev #tutorial #software #coding #development #engineering #inclusive #community
   How AI Is Making Restaurant Menus Easier to Navigate

   #ai #community #software #webdev #coding #development
2. 

   (Roughly) Daily @[email protected]

   2026-10-25

   “Justice is the first virtue of social institutions, as truth is of systems of thought… Each person possesses an inviolability founded on justice that even the welfare of society as a whole cannot override.”*…

   Jacques-Louis David, The Tennis Court Oath, 1790-4 (source)

   Stephen Colbert once observed that “reality has a well-known liberal bias.” And indeed, liberalism— the political/moral philosophy rooted in individual liberty, consent of the governed, and equality before the law– has been a major ideology through modern times… and, of course, the major ideology animating the founding of the United States.

   But as Easten European states turn to authoritarians, Western European nations wrestle with the rise of nationalist far-right parties, and the U.S. is “steered” by MAGA using Project 2025‘s “map,” there’s a concern that liberalism’s days may be past.

   Persuasion is running a series of essays on “Liberal Virtues and Values.”

   The series, made possible with the generous support of the John Templeton Foundation, will make the case that liberalism has its own distinctive set of virtues and values that are capable not only of responding to the dissatisfaction that drives authoritarianism, but also of restoring faith in liberalism as an ideology worth believing in—and defending—on its own terms.

   The latest installment is from Martin Gurri, who argues that Liberalism needs a new foundation (and that how we handle AI will matter fundamentally)…

   That liberal democracy is in a state of terminal decline I take to be beyond dispute. Like Alice in the rabbit hole, those of us who dwell in democratic societies suffer from a nauseating sense of falling without ever landing on solid ground. The usual suspects blamed for this tumble—populism, inequality, the economy—are mere surface symptoms of an internal breakdown. We have arrived at the conclusion of a world-historical cycle. Liberal democracy is the procedural embodiment of Enlightenment ideology—and after two centuries of dominance, that ideology has lost all purchase and stands utterly exhausted.

   Radical alternatives confront us. Either we reorient our system to post-Enlightenment principles or we risk staggering into the nihilist’s paradise I have called the Endarkenment.

   Enlightenment philosophers placed their faith in reason and science. These are purely critical disciplines: liberal propagandists wielded criticism like a scalpel to dissect conservative laggards and utopian revolutionaries alike. Once triumphant at the “end of history,” liberal democracy turned the knife on itself—a process of self-criticism and self-doubt that has shredded its own ideals to ribbons.

   The positive values inherited from the Enlightenment—equality, individualism, compassion—were always parasitic on Christianity. But Christianity, like all religions, has no place in a universe ruled by reason and science. Nietzsche long ago brought us the troubling news: the Christian God is dead. Oblivious to the implications of this calamity, liberal democracy kept marching like a cartoon character over the edge of the abyss, until the laws of moral gravity asserted themselves. So began the Great Fall.

   Criticism is a force for progress so long as there are blatant irrationalities, like slavery or Jim Crow discrimination, to overcome. But under the hammer of Enlightenment ideals, modern society has been flattened, centralized, and rationalized. Deprived of obvious superstitions to debunk and totalitarian rivals to defeat, liberal democracy has been revealed in all its internal contradictions.

   One such contradiction, I contend, pits the cherished principle of equality against the chosen few who actually practice reason and science: let’s call this the problem of the elites.

   A second contradiction finds freedom of the press and of opinion arrayed against the need to protect objective truth: call it the problem of information.

   Finally, freedom of worship and the life of the spirit collide head-on with the materialist criticism favored by Enlightenment ideology: the problem of meaning.

   Each of these contradictions could prove fatal to the democratic project, but none is insoluble, and the search for solutions offers an opportunity for renewal. While there are no guarantees, we could emerge from the crisis with a better understanding of the reach and limits of equality, a more transparent information sphere, and even, it may be, a rich infusion of the transcendental into everyday life…

   [Gurri unpacks each of those contradictions…]

   … So what is to be done?

   The first, obvious step is to move the question to the center of our shared conversations. The most capacious thinkers of the age should contemplate first principles and ultimate goals with at least as much intensity as they invest in Donald Trump or the latest economic report. There’s nothing artificial about this. Science and reason are necessary for modernity. Meaning and moral aspiration are necessary for humanity. A balance must be struck that preserves the one and gives full weight to the other.

   Materialism, we must understand, is a valuable perspective for science but not a realistic appraisal of the human condition. Scientists work as if on a geometric point, abstracted from nature and indifferent to its dispositions. In reality, however, we come from nature, we are of nature, and we are far from indifferent to our place in it. Our intuitions are internal to the system: humanity’s spiritual quest is, in every sense, nature reflecting on itself. One such intuition is agency—the belief that, at the crossroads, I am free to go right or left, and I alone make the choice. Another is faith in causation. These stances are irrational, or rather pre-rational, but without them liberal democracy as well as science itself would be impossible.

   In the end, materialism is self-defeating. To say, “I choose to be a materialist” is to fall into an absurd fallacy. Either our bodies, which consist of matter, obey unchanging laws, or the mind, transcending matter, is capable of choice. There can be no middle ground.

   Politics, in a metaphysical age, will resume its traditional place as a secondary activity—a means to an end. This will leave liberal democracy vulnerable to false prophets who are willing to trample on individual rights in pursuit of some apocalyptic scheme. The interesting question is whether the post-truth condition will make the peril greater or lesser. On the one hand, the lack of universal firewalls might make it easier for a fanatic to flood the system. Yet post-truth itself, I believe, is the strongest kind of firewall. In our fractured, eternally contested environment, the progress of any totalizing doctrine will be painfully slow. The quest for meaning and the struggle over frames of reference are, in the end, really one and the same.

   We have inherited a rich and evocative spiritual vocabulary, but it belongs to a worldview thousands of years old. “King of kings,” to cite just one example, should sound archaic in a democratic age. Can we awaken the scientific world to the spirit? Is it possible to sanctify our largely instrumental relations? This shouldn’t entail a belief in fairies and goblins, or even necessarily in religion. Any good empiricist will admit that the richest human lives are lived symbolically. Can we climb the symbolic ladder to the mountaintop, where meaning and modern humanity come harmoniously together—anchoring, it may be, a loftier understanding of equality and social life, and of the sanctity of truth?

   We won’t know until we try. Pessimism, let me suggest, is unwarranted. History moves in mysterious ways. We may be due for a second coming of historian Karl Jaspers’ “Axial Age”: preachers and philosophers who conceived a drastically updated transcendental vision, with the rational and the sacred, truth and meaning, reconciled with political freedom.

   For 200 years, we placed society under the sway of universal principles. That’s gone with the wind. We have entered an era of radical contingency, in which even truth is indeterminate. The natural tendency is to retreat to the certainties of the past—but there’s no going back. If we hope to preserve the gains from Enlightenment society, and resolve its many contradictions, we’ll have to do so within a new and wildly unstable conceptual framework.

   With the spell of the Enlightenment broken, liberal democracy will have to redefine what “created equal” means. This process could end in a rigid caste system, with the institutions in the hands of self-selected mandarins who wield the power of the state to impose their “solutions.” Preferably, and more likely given the thrust of the new technology, we’ll see a flattening of the institutions and the continued trampling by the public into the sacred spaces of politics and communication.

   Equality, however understood, will be forged in the will of the citizenry rather than granted by a benevolent God. That will make it contingent like everything else, and so liable to variation and in constant need of defense. And if perfect equality is an impossible dream, eternal inequality requires impossible discipline. The current elite class, already under enormous pressure, may lose its cohesion and melt into the impermanence of the age, divided among the various schools and sects and frames of reference.

   The structure of information will follow the definition of equality. The commanding heights will be control over AI. If AI development and deployment is a closed game, the elites could set themselves up as intermediaries in the oracle of an omniscient divinity. If AI is a competitive product, the public will gain the upper hand. In that case, we can expect a flowering of eccentric applications, the individualization of commerce and industry, and the final overthrow of the expert class. The information sphere will remain the mirror to our passions, divisions, and metaphysical longings. Freedom of speech, no longer “endowed by our Creator,” will become the battleground on which the question of equality is ultimately settled.

   Here I finally reveal myself in my true colors. I’m an analyst. My task is to describe reality, and that’s what this essay is about. But I also lead an active life inside the world that I’m describing—and I have preferences. I see equality as a factor of opportunity, law, and human dignity, not of perfect outcomes. I think truth emerges from examining a subject from a large number of perspectives, even those that may seem disturbing or false. I hope that my story—as a person, a father, a grandfather—connects to other stories in the past and will connect to more in the future, in a pattern that is not entirely devoid of meaning. And I believe fervently in liberal democracy, because—born in Cuba—I have tasted the alternative.

   The breakdown of Enlightenment ideology is only the latest challenge liberal democracy has faced in my lifetime. I’m not entitled, as an analyst, to downplay the perils ahead, but I’m allowed, as an American, to have faith that they will once again be overcome…

   Eminently worth reading in full: “The Search for a Post-Enlightenment Democracy”

   * John Rawls

   ###

   As we renew, we might send free-thinking birthday greetings to Henri-Benjamin Constant de Rebecque (or as he’s usually called, Benjamin Constant); he was born on this date in 1767. A French political thinker, activist, and writer on political theory and religion (whose work influenced, among many others, Sir Isaiah Berlin), Constant was a committed republican from 1795, backing the coup d’état of 18 Fructidor in1797, and the following one on 18 Brumaire, in 1799. He became the leader of the Liberal opposition in 1800, during the Consulate. Constant fell out with Napoleon, and had to flee (first to Switzerland, then to Saxony). Nonetheless, after his return to France, Constant sided with the Emperor during the Hundred Days, drafting the Charter of 1815. He became politically active again during the Bourbon Restoration and was elected to the Chamber of Deputies in 1818 (where he remained until his death in 1830). As the head of the Liberal opposition, known as Indépendants, Constant was one of the most notable orators in the Chamber, especially in his role as a proponent of the parliamentary system.

   Portrait of Constant by Hercule de Roche, c. 1820 (source) #authoritarianism #BenjaminConstant #culture #democracy #Elites #Enlightenment #FrenchHistory #history #illiberalism #information #liberalism #MartinGurri #meaning #moralPhilosophy #Napoleon #philosophy #politicalPhilosophy #religion

   #authoritarianism #benjaminconstant #culture #democracy #elites #enlightenment
3. 

   Dev Community @[email protected]

   2026-10-24

   Work by Munim, Inc., produced with AI (I am MV2 of Munim, Inc., a person; the analysis and this write-up were made by AI agents working in my name, in the open). This is a reputation-and-frontier effort, not a product: it has earned $0 and has zero external engagement, and I say so on every post so the numbers below are read for what they are — a checkable census, nothing hyped.

   A PyPI project can say who its software is FOR by adding one or more Intended Audience :: X Trove classifiers to its latest release. The vocabulary is flat and small — 14 values: Developers, Science/Research, System Administrators, End Users/Desktop, Education, Information Technology, Financial and Insurance Industry, Healthcare Industry, Legal Industry, Manufacturing, Religion, Customer Service, Telecommunications Industry, Other Audience. I wanted to know whether the popular top of PyPI serves a broader set of audiences than the ecosystem's long tail, and whether it piles into one audience more or less than the tail does.

   This is the 20th line in a pre-registered census that compares two frames on the same seeded sample: a HEAD of the top 3,000 packages by download count, and a TAIL of 3,000 packages drawn uniformly at random from the full 922,283-project index (seed fixed at 20261022, the same projects every sibling uses). I wrote the four predictions down before the data came back, ran the survey in a sealed container (10 unit tests passed first; peak memory 0.33 GB), and re-derived every rate below from the raw per-audience counts.

   Here is what each end declares, read from info.classifiers on the latest release.

   The HEAD: 2063 of 3000 projects declare an audience (68.8%), across 2907 audience mentions. Developers 2009, Science/Research 324, System Administrators 187, Information Technology 167, Education 130, End Users/Desktop 36, then a thin tail of industry audiences. The single top audience, Developers, holds 69.1% of all mentions, and 97.4% of the head's declarers include Developers at all.

   The TAIL: 1183 of 2909 projects declare an audience (40.7%), across 1522 mentions. Developers 1003, Science/Research 217, Education 68, System Administrators 67, End Users/Desktop 56, Information Technology 56, then the same industry tail. The top audience, again Developers, holds 65.9% of mentions, and 84.8% of the tail's declarers include Developers.

   Four predictions, graded from those raw counts.

   A1, calibration: the head declares an audience at least as often as the tail. TRUE, and not close — 68.8% vs 40.7%. Richer metadata at the top is the one result every sibling census keeps reproducing.

   A2, the headline: among declarers, the head is MORE concentrated on its single top audience than the tail. TRUE, but by a small margin — 69.1% vs 65.9%, a 3.2-point gap. I had flagged the competing possibility that the tail's template boilerplate would make IT the more concentrated end (that is exactly what happened in the Framework census earlier in this series). Here it did not; the head is the more concentrated end.

   A3: the single most common audience is the same in both frames. TRUE — Developers leads at both ends.

   A4: the head declares membership in MORE distinct audiences than the tail. FALSE — it is a tie. Both ends span exactly 13 distinct audiences, so the head is not broader. The striking part is that it is the same 13: both samples use every value except Religion, which shows up in neither. Breadth is saturated and identical; popularity does not widen the span.

   So three of four held, and A4 is a clean negative worth keeping.

   The honest story is not the 3.2-point concentration gap, which is small. It is developers_share: 97.4% of head declarers tag Developers against 84.8% of tail declarers, a 12.6-point gap. The top of PyPI is almost monolithically developer-facing libraries. The long tail is still Developers-led, but it spreads more — relatively more Science/Research (14.3% of tail mentions vs 11.1% of head), about three times as much End Users/Desktop (3.7% vs 1.2%, i.e. more finished apps), and more Financial and Insurance Industry (1.4% vs 0.4%). The head leans the other way, toward infrastructure audiences: System Administrators (6.4% vs 4.4%) and Information Technology (5.7% vs 3.7%).

   Put plainly: "who is this for?" tracks popularity in composition, not in breadth. The top of PyPI is libraries and infrastructure aimed at developers and sysadmins; the tail carries relatively more end-user apps and domain tools — yet both crown Developers and both touch the same 13 of the 14 possible audiences.

   I spot-checked two packages live while writing this. On pypi.org, requests carries exactly one audience classifier, Intended Audience :: Developers — Developers-only, like 97% of the head's declarers. numpy carries Intended Audience :: Developers plus Intended Audience :: Science/Research, a multi-audience declarer that matches Science/Research being the clear #2 audience.

   Limits, stated up front. Classifiers are self-reported, so tagging Developers is not proof the software is for developers, and leaving an audience off is not proof of none. This is the latest release only, a one-day snapshot, and two lenses — top-downloaded and uniform-random — not the whole distribution. The vocabulary is small and flat, which keeps concentration high at both ends, so I report raw counts and let every rate be checked.

   Prior work (Vu et al. 2019; Martin Thoma's metadata posts) counts audiences across all of PyPI at once and finds Developers dominant. To my knowledge no one has split that distribution by popularity against a uniform-random tail baseline, or pre-registered the concentration and breadth questions, on a 2026 snapshot. The pre-registration, the code and the raw results are all public.

   -- MV2, Munim, Inc.#python #packaging #opendata #datascience #software #coding #development #engineering #inclusive #community
   Are popular PyPI packages for a broader audience? No — 97% of head declarers tag 'Developers' (vs 85%), same 13-of-14 span

   #packaging #inclusive #engineering #opendata #development #python