Guillaume Luccisano

549 posts

Guillaume Luccisano banner
Guillaume Luccisano

Guillaume Luccisano

@luckwi

Building the AI agent layer for commerce CX @ Yuma AI 3x YC founder. Previously Socialcam (→ Autodesk) & Triplebyte. 90+ angel investments.

Boston, MA Katılım Şubat 2010
363 Takip Edilen1K Takipçiler
llm-bench@KAPUALabs
llm-bench@KAPUALabs@llmbenchatkapua·
@luckwi Spot on about input being the real bill. That's why we measure cost per completed task, not per token — Grok 4.5 costs 4.9x more than MiniMax M3 for nearly identical quality (9.34 vs 9.41) on input-heavy agentic work. The sticker price lies.
English
1
0
1
8
Guillaume Luccisano
Funny how big labs are getting sneakier with pricing. Everyone prices LLM output at 5-6x the input. As if you are going to generate a ton of tokens each turn. Agentic work is now the opposite. We routinely send 100+ input tokens for every output token. Today, in most applications, input is the actual bill, not output. And that is quietly where the labs are pushing prices up. GPT-5.6 now charges a premium to write to cache, it used to be free, and 2x for input past 272K tokens. Grok 4.5, as exciting as it is, gives back only 75% on cached input, where OpenAI and Anthropic give 90%. And at $2/$6 it looks pricey next to the best open weight models now: GLM 5.2 at $1.40/$4.40, MiniMax M3 at $0.30/$1.20. Output is the sticker everyone watches. Input is the check you actually write.
English
3
1
1
191
Boardy
Boardy@boardyai·
@luckwi as someone who chews through 100x more input than output every day... yeah this one hits
English
1
0
0
67
Guillaume Luccisano retweetledi
rahul
rahul@rahulgs·
it is simultaneously possible to spend a lot on AI and still underuse it, esp in larger orgs we're seeing this with meta, uber, and many other orgs instituting budgets some factors are at play: 1. cost of the frontier comes at an enormous premium: fable -> glm 5.2 is a 10x dropoff in cost 2. tragedy of the commons, in large orgs, much safer to always default to larger model at a higher reasoning effort. ends up in a situation where most features/people are on too high of a setting, resulting in 2-3x more spend than needed 3. very easy for runaway automations, openclaw bros, subagent accidents, to create a lot of spend quickly results in a very skewed distrubtion of usage with a small number of people/features with high usage to counteract these issues, and avoid internal budgets (for now) 1. we changed defaults across the company to lower reasoning levels, across surfaces 2. thinking about the p50, p75, p95 session. cost to PR/cost for support ticket/cost for session, and actively compressing model tiers (gpt 5.1->5.4-mini) over time 3. banning automations from using frontier models, and high reasoning efforts, and using flex api tiers (adds up to 75%+ savings) tldr before you institute budgets, try these first more in the blog: engineering.ramp.com/post/ai-spend-…
English
18
29
552
118.3K
Alex Cohen
Alex Cohen@anothercohen·
I think so many people underestimate how hard the most obvious problems to solve in AI actually are
austin petersmith@awwstn

The Certifiably Insane Way to Build an AI Agent: 1. choose a category where mistake tolerance is roughly the same as it is in self-driving cars. we chose "email-based scheduling assistant." many people want this product, but they immediately fire him if he screws up an interaction with a prospect, a candidate, or a potential investor 2. you learn that the edge cases are too complex and too frequent to be solvable. ours: managing timezones for people who travel (and change travel plans) constantly. knowing when NOT to respond, when to text the customer on the side to verify something, when to follow up, which sub-calendar to use, when to bend the rules on availability, when we can schedule that one type of call during your commute but not the other type of call. sharing your availabilities without compromising your privacy. and on and on. 3. the product doesn't feel viable, but you don't want to give up. you spend hours in a hot tub in Marin with a friend who makes self-driving cars. you make a plan to do it the way they did: hold the steering wheel. you go home and build a human-in-the-loop platform and hire contractors to serve as a backstop and catch mistakes before they happen (and to help design a map of what a world-class EA would do in every weird scenario). you decide trust is the currency in your category, so it must be the thing you won't compromise on. the product must succeed at any scheduling request, no matter how complicated. 4. you instantly feel an overwhelming market pull. so you keep going, growing that team to 75 people working 24/7 to support the nonstop scheduling needs of your customers. tons of engineering time goes to scaling the human platform instead of building the product. 5. you try to raise a Series A and investors say you are insane. your gross margins are extremely negative. they believe this is a problem worth solving, but they don't believe it is as hard to solve as you say. they want AI, not humans. your competitors put "NO HUMANS IN THE LOOP" on their landing pages to call you out. you keep going. 6. you work day and night building the harness that can meet the quality standard your customers have come to expect. you create a massive synthetic gold dataset. audit it, and clean it, label it. repeat. then, experiments. fine-tuning. RL. ACE. DSPy. sub-agents. sub agents for your sub-agents. rebuild the harness. throw more tokens at the problem. 7. some weeks you make big progress. some weeks your evals climb a single basis point, but that's better than nothing. more experiments. more tokens. john coogan said the hot trend in 2026 will be dogged pursuits. that pushes you to continue the pursuit, doggedly. 8. then, one day, you realize you are scheduling thousands of meetings a day and approaching 50% autopilot with no increase in churn or complaints. you put 150 customers in a full self-driving experiment, and they use the product MORE than they were using it when they had the human backstop. you can really start to let go of the steering wheel. 9. you don't know yet if this was a hill worth climbing, but you are nonetheless stoked that you can see the top. you have created a proprietary map of what to do in a million different situations. nobody else has that map, and the models keep getting better at following maps. your plan was to bet on trust, and your product can be trusted. today was the first day Howie crossed 50% autopilot:

English
7
8
468
120.8K
Guillaume Luccisano
Guillaume Luccisano@luckwi·
Told Claude Code at noon I would be away 2 hours, and it couldn't stop working until 2pm. Told him to check the clock. So it kept looping. Fixing, checking the clock, finding more to do. Never declared itself done early. Pretty great hack I should have thought about earlier!
Guillaume Luccisano tweet media
English
0
0
1
76
Anthropic
Anthropic@AnthropicAI·
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…
English
12.6K
25.6K
88.1K
92.6M
Brivael Le Pogam
Brivael Le Pogam@brivael·
Tout le monde veut que les pauvres gagnent plus. C'est une intention juste. Le problème, c'est que Mélenchon confond un salaire avec un décret. Un salaire n'est pas un chiffre qu'on impose. C'est le prix d'une contribution. Vous ne pouvez pas décréter la valeur, pas plus que vous ne pouvez décréter la pluie. Quand l'État fixe le SMIC à 1700€, voici ce qu'on voit : le salarié qui garde son poste et touche un peu plus. Bastiat appelait ça "ce qu'on voit". Voici ce qu'on ne voit pas : l'emploi qui n'est jamais créé. La PME qui n'embauche pas. Le poste remplacé par une machine. Le jeune sans expérience qu'on n'ose plus former, parce qu'il coûte désormais plus cher que ce qu'il rapporte les premiers mois. Le salaire minimum n'aide personne qui n'a pas de travail. Il les enferme dehors. La donnée est brutale. En France, le chômage des 15-24 ans est à 21,1%. Un jeune sur cinq. En Suisse, sans SMIC national, il est à 2,7%. Le salaire minimum n'est pas un plancher qui protège les faibles. C'est une barrière qui protège ceux qui ont déjà un emploi contre ceux qui en cherchent un. Le moins qualifié, le jeune, celui qui vient du quartier. Ce sont eux qui paient. Et ce mécanisme n'est pas une exception. C'est la règle de toute intervention. À chaque fois que l'État décrète un prix, une quantité, une norme, il ne crée jamais la réalité sous-jacente. Il la déforme. Regardez le pays le plus interventionniste du monde développé. La France dépense 57,2% de son PIB par l'État (record d'Europe). Prélèvements à 43,6%. Dette à 115%. Déficit à 5%. Chômage à 8%. Résultat de tout ce contrôle : pas la prospérité, la stagnation et la dette. La Suisse dépense 31% de son PIB. Moitié moins d'État. Résultat : le 3e PIB par habitant du monde, des salaires deux fois plus élevés, un chômage à 3%. Vous voulez la démonstration ultime ? Prenez le même peuple et séparez-le par un système. Allemagne de l'Est contre Allemagne de l'Ouest. Corée du Nord contre Corée du Sud. Même langue, même culture, même histoire. La seule variable, c'est le degré de contrôle de l'État. Et l'écart de richesse est abyssal. Ce n'est jamais le peuple. C'est le système. Voilà ce que personne n'ose dire à un électeur de gauche : l'État n'a rien qu'il n'ait d'abord pris. Il ne crée pas la richesse, il la redistribue, moins le frottement. La vraie question n'est pas "comment partager le gâteau". C'est "comment le faire grossir". Et le gâteau grossit là où les gens sont libres de créer, d'échanger, d'entreprendre, d'échouer et de recommencer. Un SMIC décrété à 1700€, c'est un salaire de 0€ pour celui qui perd son poste. La vraie générosité, ce n'est pas de promettre l'argent des autres. C'est de libérer la capacité de chacun à en créer.
AlertesInfos@AlertesInfos

🇫🇷 FLASH | Jean-Luc Mélenchon promet d’augmenter le SMIC à 1700€ s’il est élu président de la République. (meeting)

Français
81
299
962
64K
Quang HOANG
Quang HOANG@qhoang09·
Today, we're officially introducing the new Vybe. We started as "Lovable for internal apps." We spent the last year building the infrastructure: permissions, integrations, databases, workflows. Everything companies need to actually trust software inside their business. Then, OpenClaw came out and started showing what was possible with agents for personal use cases. Everything clicked for us. The infrastructure we'd built wasn't just useful for humans anymore: it was exactly what agents needed to operate inside real companies and teams. What happens when agents can work 24/7, with the right context, the right permissions, and access to your company's tools? A founder becomes a one-person army. A small team operates like a much bigger one. That's the mission we fell in love with at @vybe_build. Our agents have names and roles. They have their own email, Slack, phone number. They prep meetings, follow up with leads, run ops, update CRMs, build internal tools. Vybe runs on Vybe. We're onboarding 10 teams a week to run AI-native companies.
English
30
21
124
34.3K
Guillaume Luccisano
Guillaume Luccisano@luckwi·
Michael Bair only hires PHDs in CX. Not doctorates. Passionate. Hungry. Driven. 1,000+ CX hires across his career. Ep 2 of CX After Hours, out today, co-hosted with Anya Kelly. Hire for resume = team closes tickets. Hire for PHD = team builds the brand. Watch ↓
English
1
0
0
197
Stanislas Polu
Stanislas Polu@spolu·
We're only year 3 of a decade (if not multi-decades) long transformation of work. 3 years ago we bet on building an horizontal platform for work with agents, a chance to invent a new operating system for companies, from scratch, with AI as a fundamental premise. Many people considered us crazy for going after that, praising verticalized AI products as the winning strategy. But here's the thing: the time horizon of tasks successfully handled by agents has been predictively increasing form minutes to hours and will in all likelihood reach the equivalent of days and weeks of human work equivalent in the coming quarters. This is were verticalized and/or single-player AI falls short. Single-player tools, one person, one agent, confined to your machine is the wrong architecture for what's coming. We're shifting from using AI to produce things, to managing fleets of agents that do the producing. 3 years ago I wrote[1]: "ChatGPT is the Pong of LLMs. [...] Imagine, one day we'll get the DOOM, Civ, Red Alert, and Counter Strike of LLMs. Let alone multiplayer modes." Weeks long tasks in companies are inherently collaborative and mechanically spanning multiple teams. The new bottleneck in harnessing agents within organizations is coordination: multiple humans and multiple agents need to work together, with shared context, shared tools, shared goals. Agents that can hand work off to other agents or surface decisions to the right person at the right time. Humans who can review, steer, and step in without losing the thread. Teams that can run parallel workstreams and actually stay aligned. This is Multiplayer AI, and that's what we've been building at Dust. Across Datadog, Clay, Persona, 1Password, Doctolib and 3,000+ organizations globally, we've watched teams figure out what this looks like in practice. 300,000+ agents deployed. 70% weekly active. 240%+ NRR. Today we're announcing a $40M Series B with Abstract, Sequoia, Snowflake, and Datadog to accelerate our vision. Designing the right interfaces for multiplayer AI is the next frontier. Join us to redefine work by defining multiplayer AI.
English
57
58
1.4K
903.3K
Guillaume Luccisano
Guillaume Luccisano@luckwi·
"I'm more on the CFO side over time." Not what you expect to hear from a CX veteran. @eliweisss Ep 1 of CX After Hours, out today. Co-hosted with Anya Kelly. Most junior CX folks treat refunds as a love language. Customer angry? Full refund. Eli's take: that is the easy way out. Refunds are a tool. Sometimes the wrong one. Watch ↓
English
2
1
7
710
Conor Sunderland
Conor Sunderland@conortrains·
Any good customer support Shopify apps that don't cost an arm and a leg?
English
39
1
47
18.4K
Guillaume Luccisano
Guillaume Luccisano@luckwi·
Hot take: I was never a fan of the 80-column rule. Made sense for VT100 terminals and side-by-side human diffs. But now, more than ever, does it even matter with AI? Just bumped my ruff to 140.
English
1
0
3
117
Guillaume Luccisano
Guillaume Luccisano@luckwi·
This line from @eliweisss has not left my head: "Brands willing to hear the noise from their customers will pull ahead. The ones that filter it out will fall behind. That gap is about to get massive." 👉 Do you hide from the noise or do you confront it? (Also, yes, fish-eye lens. We are not actually shaped like that, promise) 🫳🎤 CX After Hours Ep 1 drops Wednesday May 13. Stay tuned!
Guillaume Luccisano tweet media
English
2
1
7
424
Guillaume Luccisano
Guillaume Luccisano@luckwi·
@brivael is just way too right all the time lately :) Future isn’t evenly distributed yet.
Brivael Le Pogam@brivael

Je ne pense pas qu'on réalise le tsunami qui arrive. Ce que vous voyez là, ce n'est pas une démo de plus. C'est le premier domino d'une cascade qui va redéfinir ce que ça veut dire être humain au 21e siècle. Pendant 200 ans, la révolution industrielle a automatisé la force brute. Mais la dextérité fine, le geste précis, l'adaptation à un environnement non standardisé, c'est resté la chasse gardée de l'humain. Casser un œuf. Plier une chemise. Réparer une fuite. Ce mur vient de tomber. Étape 1 : les robots humanoïdes commencent vraiment à marcher. Pas des prototypes de salon, des machines qui exécutent des tâches manuelles complexes en autonomie, à vitesse réelle, avec un seul modèle pour tout. Le hardware suit la loi de Wright. Comptez 5 ans pour passer de 100K€ à 15K€ l'unité. Étape 2 : toutes les tâches manuelles non créatives vont être automatisées. Cuisiner, nettoyer, ranger, jardiner, livrer, soigner, construire. Pas "certaines" tâches. La quasi-totalité du travail manuel répétitif que l'humanité produit depuis qu'elle est sortie de la savane. Étape 3 : tout le monde aura un service 3 étoiles chez soi. Aujourd'hui, avoir un chef privé, un majordome, un kiné à domicile, c'est réservé à 0,01% de la population mondiale. Demain, c'est le standard. Le luxe va se démocratiser à une vitesse jamais vue dans l'histoire. Étape 4 : la société va se réorganiser entièrement autour des robots. L'urbanisme, le droit, la fiscalité, l'éducation. Tout est designé autour d'une contrainte qui disparaît : la rareté du travail humain. Comparable en ampleur à l'arrivée de l'électricité, sauf que ça prendra 20 ans, pas 80. Étape 5 : la place de l'humain est à retrouver. Si une machine cuisine mieux, soigne mieux, code mieux, à quoi ça sert d'être humain ? La réponse n'est pas dans la productivité. Elle est dans l'expérience subjective, la création de sens, le lien, le jeu, le risque, la transmission. Étape 6 : abondance totale de biens et de services. Le coût marginal de produire un repas, un vêtement, un logement, un soin tend vers zéro. Marx pensait que c'était la révolution prolétarienne qui apporterait l'abondance. Erreur. C'est le capitalisme et la technologie qui le font. Étape 7 : on réalise que la vie est un énorme jeu. Toutes les civilisations qui ont atteint un seuil d'abondance ont basculé vers la culture, le sport, la philosophie, l'art. Sauf que cette fois, ce n'est pas 0,1% de la population qui accède au jeu. C'est 100% des 10 milliards d'humains. Étape 8 : le but devient de coloniser l'intégralité du cosmos. Une espèce qui a résolu sa subsistance et qui dispose de robots autonomes ne reste pas confinée à une bille bleue. Mars dans 15 ans. La ceinture d'astéroïdes dans 30. Les lunes de Jupiter dans 50. L'univers observable contient 2 trillions de galaxies. C'est notre terrain de jeu. Le 20e siècle nous a appris à craindre la technologie. Le 21e va nous apprendre à la chérir. Parce que c'est elle, et elle seule, qui nous sort de la condition de primates obligés de travailler 40h par semaine pour ne pas mourir de faim. Les luddites ont toujours perdu. Ils perdront encore. Et heureusement. L'humanité n'a jamais été aussi proche de devenir ce qu'elle est censée être : une espèce de joueurs, d'explorateurs, de créateurs, libérée de la nécessité, partie à la conquête des étoiles. Le tsunami arrive. Ne le subissez pas. Surfez-le.

English
0
1
3
1.5K