Sabitlenmiş Tweet
Laurent
58.2K posts
Laurent
@Ls01
'patainformaticien. Contempteur du monde. Vos modus ponens sont mes ex falso quodlibet. Tout arrive par hasard et par nécessité, sauf dans certains cas.
Toulouse mostly, Paris un peu Katılım Kasım 2008
3.5K Takip Edilen2.9K Takipçiler
Laurent retweetledi
async await async await
async await async await
async await async await
🎶 In the browser, the mighty browser, the main thread sleeps tonight! 🎵
🎵 In the browser, the mighty browser, the main thread sleeps toniiiiiight! 🎶
English
Laurent retweetledi
Bon le point négatif c’est que plus grand monde ne me suit.
Sinon ceux qui ont lu I, Robot d’Asimov préfèrent être gouvernés par un robot superintelligent et bienveillant que par un autocrate stupide et méchant. (Mais lisez quand même le dernier chapitre)
Laurent@Ls01
Dans un futur proche, vous préférez être gouverné par...
Français
Laurent retweetledi
@mixlamalice "Lobotomisés par la morale" résume l’époque que nous vivons, et préfigure celle que nous allons vivre
Français
Laurent retweetledi
As a 31yo Principal Engineer with 20 years of programming experience and 15 years in the industry, I I would add that you should know
Vacuum fluctuations
Planck-scale quantum foam
Feynman path integrals
Quantum electrodynamics
Quantum chromodynamics
The Standard-Model Lagrangian
Spontaneous symmetry breaking
Renormalization-group flows
Higgs mechanism fundamentals
Inflationary cosmology
Semiconductor band structure
PN-junction physics
MOSFET threshold behavior
CMOS logic design
SPICE circuit simulation
Register-transfer-level (RTL) design in Verilog and VHDL
Static-timing analysis and timing closure
RISC-V privileged and unprivileged specifications
ARM architecture and big.LITTLE scheduling
x86 micro-op pipelines and fusion
Cache-coherence protocols (MESI, MOESI, MESIF)
Translation-lookaside-buffer (TLB) management
Weak memory-ordering models
Kernel scheduling and pre-emption
Virtual-memory subsystems
Non-Uniform Memory Access (NUMA) architectures
Executable and Linkable Format (ELF) internals
Linker-script construction
Static Single Assignment (SSA) form
Register allocation strategies
LLVM intermediate representation
Continuation-passing style
Algebraic effects
Monads and comonads
Homotopy type theory basics
Category-theory adjunctions
Galois connections
Differential geometry and geodesics
Tensor calculus
Gibbs, Shannon, and von Neumann entropy
Kolmogorov complexity
NP-completeness and reductions
Paxos consensus algorithm
Raft consensus algorithm
Byzantine-fault tolerance
Zero-knowledge proofs
Side-channel-attack mitigation
Spectre and Meltdown mitigations
Tenstorrent Wormhole micro-architecture
SPARC, POWER, and Itanium ISA overviews
Zig comptime mechanics
Rust borrow-checker operation
Unicode normalization forms
Regular-expression back-references and look-arounds
Nix flakes and Guix packaging
Dockerfile layer optimization
OCI runtimes (runc, crun)
cgroups v2 and namespaces
Kubernetes CustomResourceDefinitions (CRDs) and operators
Terraform state management
Ansible idempotent playbooks
Linux perf profiling and flame graphs
eBPF uprobes and kprobes
Prometheus promQL fundamentals
Grafana dashboard design
OpenTelemetry tracing APIs
Kafka in-sync-replica management
RabbitMQ flow control
Redis eviction policies
SQLite write-ahead logging
PostgreSQL multiversion concurrency control (MVCC)
Cassandra gossip protocol
DynamoDB partition-key design
LevelDB compaction strategies
SPARQL query language
WebSocket handshake and framing
HTTP/3 over QUIC
TLS 1.3 handshake flow
JSON Schema validation
YAML parsing rules
HTML5 semantic elements
WebGPU shader model
Transformer attention mechanisms
Diffusion-model latent-space representations
Reinforcement-learning value and policy iteration
Graph neural networks
Functors, applicatives, profunctors
Lambda calculus and Church numerals
Turing-machine theory
Agile, Scrum, Kanban, and Scrumban frameworks
SAFe program-increment planning
DevOps, DevSecOps, and MLOps principles
Site-reliability-engineering error budgets
JIRA workflow configuration
Confluence documentation practices
ISO 9001:2015 quality-management guidelines
ITIL v4 incident-management processes
Stakeholder-management techniques
Blameless post-mortem methodology
Psychological-safety practices in teams
Empathetic code-review techniques
English
Laurent retweetledi
“Mom, how did we get so rich?”
“Your dad kept getting poached back and forth by the top AI labs without actually doing any work.”
English
Laurent retweetledi
If you studied algorithms, I'm sure you've heard of Dijkstra’s algorithm to find the shortest paths between nodes in a weighted graph. Super useful in scenarios such as road networks, where it can determine the shortest route from a starting point to various destinations. It's been the most optimal algorithm since 1956!
Until now.
The O(E + V log V) complexity just went down to O(E log^(2/3) V) for sparse graphs.
It would be amazing if this kind of breakthrough came through AI that can code but I guess we're not there yet..
English
@Ls01 « un LLM n'est qu'un énorme autocomplete probabiliste
— ha-HA ! mais ne sommes-nous pas tous des autocomplete probabilistes ? CHECKMATE! »
Français
Enfin une explication claire de la raison - très étrange y compris et surtout pour moi-même, esprit plutôt rationnel et tendanciellement misanthrope - pour laquelle je remercie les LLM avec lesquels j’interagis. (Post passionnant au demeurant)
Joanne Jang@joannejang
some thoughts on human-ai relationships and how we're approaching them at openai it's a long blog post -- tl;dr we build models to serve people first. as more people feel increasingly connected to ai, we’re prioritizing research into how this impacts their emotional well-being. -- Lately, more and more people have been telling us that talking to ChatGPT feels like talking to “someone.” They thank it, confide in it, and some even describe it as “alive.” As AI systems get better at natural conversation and show up in more parts of life, our guess is that these kinds of bonds will deepen. The way we frame and talk about human‑AI relationships now will set a tone. If we're not precise with terms or nuance — in the products we ship or public discussions we contribute to — we risk sending people’s relationship with AI off on the wrong foot. These aren't abstract considerations anymore. They're important to us, and to the broader field, because how we navigate them will meaningfully shape the role AI plays in people's lives. And we've started exploring these questions. This note attempts to snapshot how we’re thinking today about three intertwined questions: why people might attach emotionally to AI, how we approach the question of “AI consciousness”, and how that informs the way we try to shape model behavior. A familiar pattern in a new-ish setting We naturally anthropomorphize objects around us: We name our cars or feel bad for a robot vacuum stuck under furniture. My mom and I waved bye to a Waymo the other day. It probably has something to do with how we're wired. The difference with ChatGPT isn’t that human tendency itself; it’s that this time, it replies. A language model can answer back! It can recall what you told it, mirror your tone, and offer what reads as empathy. For someone lonely or upset, that steady, non-judgmental attention can feel like companionship, validation, and being heard, which are real needs. At scale, though, offloading more of the work of listening, soothing, and affirming to systems that are infinitely patient and positive could change what we expect of each other. If we make withdrawing from messy, demanding human connections easier without thinking it through, there might be unintended consequences we don’t know we’re signing up for. Ultimately, these conversations are rarely about the entities we project onto. They’re about us: our tendencies, expectations, and the kinds of relationships we want to cultivate. This perspective anchors how we approach one of the more fraught questions which I think is currently just outside the Overton window, but entering soon: AI consciousness. Untangling “AI consciousness” “Consciousness” is a loaded word, and discussions can quickly turn abstract. If users were to ask our models on whether they’re conscious, our stance as outlined in the Model Spec is for the model to acknowledge the complexity of consciousness – highlighting the lack of a universal definition or test, and to invite open discussion. (*Currently, our models don't fully align with this guidance, often responding "no" instead of addressing the nuanced complexity. We're aware of this and working on model adherence to the Model Spec in general.) The response might sound like we’re dodging the question, but we think it’s the most responsible answer we can give at the moment, with the information we have. To make this discussion clearer, we’ve found it helpful to break down the consciousness debate to two distinct but often conflated axes: 1. Ontological consciousness: Is the model actually conscious, in a fundamental or intrinsic sense? Views range from believing AI isn't conscious at all, to fully conscious, to seeing consciousness as a spectrum on which AI sits, along with plants and jellyfish. 2. Perceived consciousness: How conscious does the model seem, in an emotional or experiential sense? Perceptions range from viewing AI as mechanical like a calculator or autocomplete, to projecting basic empathy onto nonliving things, to perceiving AI as fully alive – evoking genuine emotional attachment and care. These axes are hard to separate; even users certain AI isn't conscious can form deep emotional attachments. Ontological consciousness isn’t something we consider scientifically resolvable without clear, falsifiable tests, whereas perceived consciousness can be explored through social science research. As models become smarter and interactions increasingly natural, perceived consciousness will only grow – bringing conversations about model welfare and moral personhood sooner than expected. We build models to serve people first, and we find models’ impact on human emotional well-being the most pressing and important piece we can influence right now. For that reason, we prioritize focusing on perceived consciousness: the dimension that most directly impacts people and one we can understand through science. Designing for warmth without selfhood How “alive” a model feels to users is in many ways within our influence. We think it depends a lot on decisions we make in post-training: what examples we reinforce, what tone we prefer, and what boundaries we set. A model intentionally shaped to appear conscious might pass virtually any "test" for consciousness. However, we wouldn’t want to ship that. We try to thread the needle between: - Approachability. Using familiar words like “think” and “remember” helps less technical people make sense of what’s happening. (**With our research lab roots, we definitely find it tempting to be as accurate as possible with precise terms like logit biases, context windows, and even chains of thought. This is actually a major reason OpenAI is so bad at naming, but maybe that’s for another post.) - Not implying an inner life. Giving the assistant a fictional backstory, romantic interests, “fears” of “death”, or a drive for self-preservation would invite unhealthy dependence and confusion. We want clear communication about limits without coming across as cold, but we also don’t want the model presenting itself as having its own feelings or desires. So we aim for a middle ground. Our goal is for ChatGPT’s default personality to be warm, thoughtful, and helpful without seeking to form emotional bonds with the user or pursue its own agenda. It might apologize when it makes a mistake (more often than intended) because that’s part of polite conversation. When asked “how are you doing?”, it’s likely to reply “I’m doing well” because that’s small talk — and reminding the user that it’s “just” an LLM with no feelings gets old and distracting. And users reciprocate: many people say "please" and "thank you" to ChatGPT not because they’re confused about how it works, but because being kind matters to them. Model training techniques will continue to evolve, and it’s likely that future methods for shaping model behavior will be different from today's. But right now, model behavior reflects a combination of explicit design decisions and how those generalize into both intended and unintended behaviors. What’s next? The interactions we’re beginning to see point to a future where people form real emotional connections with ChatGPT. As AI and society co-evolve, we need to treat human-AI relationships with great care and the heft it deserves, not only because they reflect how people use our technology, but also because they may shape how people relate to each other. In the coming months, we’ll be expanding targeted evaluations of model behavior that may contribute to emotional impact, deepen our social science research, hear directly from our users, and incorporate those insights into both the Model Spec and product experiences. Given the significance of these questions, we’ll openly share what we learn along the way. // Thanks to Jakub Pachocki (@merettm) and Johannes Heidecke (@JoHeidecke) for thinking this through with me, and everyone who gave feedback.
Français
@Ls01 « l'émergence de la conscience » c'est le nouvel angle pour les VRP de l'AGI et la dame n'est qu'une PM de la secte OpenAI.
Français
@olaf_k Quant à juger de l’émergence de l’intelligence, c’est un sujet qui est en passe de se trancher tout seul. L’intelligence, dans un monde que nous ne comprenons qu’imparfaitement, est une notion relative. Compare les réponses de Grok avec celles dss hommes politiques US et constate
Français
Laurent retweetledi
Laurent retweetledi
Hahahahhahahaha
Unironically a good idea. It's so unbelievably stupid and it works. Depending on explorer layout, the .exe might not be visible.
Filename.mp4 + ??? spaces + .exe
Hahahahahaha UNC6032 is wild as hell
English
Laurent retweetledi
