Hi, I'm Luis 👋

Recently, economists have begun theorizing about a world in which AI models handle many or most transactions on humans’ behalf. We thought we’d run a new experiment—Project Deal—to learn more about this in practice.

For one week, we created a classified marketplace for employees in our San Francisco office—like Craigslist, but with a twist: all of the deals were conducted by AI models acting on our employees’ behalf. In December 2025, Claude interviewed people about which of their personal belongings they might want to sell and what sorts of things they might be willing to buy. We incentivized participation by giving everyone’s agent $100 to spend. Then, our employees’ Claude agents made postings vying for each other’s attention. Negotiations commenced. Deals were made, closets decluttered. At the end of it all, people brought in and exchanged the actual, physical goods that were haggled over by their AI avatars—covering everything from a snowboard to a plastic bag full of ping-pong balls.

We were struck by how well Project Deal worked. Our AI agents struck 186 deals at a total transaction value of just over $4,000. To our surprise, participants were very enthusiastic about the experience—they even stated a willingness to pay for a similar service in the future.

But we also ran a parallel experiment (this one in secret). We tested how our participants would fare if we varied which Claude model represented them. We compared our then-frontier model, Claude Opus 4.5, to our smallest model, Claude Haiku 4.5. We found that agent quality does make a difference: people represented by “smarter” models got objectively better outcomes. Yet our post-experiment survey found that those with weaker models didn’t notice their disadvantage.

To be sure, this was a pilot experiment with a self-selected participant pool. But we suspect we’re not far from more agent-to-agent commerce bubbling up in the real world, with real consequences.

The first thing to say is that our experiment worked. It is possible for AI agents to represent humans in a marketplace. In our “real” run, our 69 agents struck 186 deals across over 500 listed items, for a total transaction value of just over $4,000. And these were far from trivial, one-click deals. Agents had to identify potential matches, propose prices, field counteroffers, and reach agreement—all in natural language, without a prebaked negotiation protocol. When our surveyed participants rated the fairness of the individual deals, the scores were unremarkable, in the best possible sense: on a scale from 1 (unfair to one party) to 7 (unfair to the other), they hovered around 4—right in the middle. On this and other measures, people reported they were broadly satisfied with how their agents represented them.

But not every agent did equally well.
When we looked at the two runs with a mix of Opus and Haiku agents, we found that Opus outperformed Haiku on most objective measures.

There was clearly a quantitative disadvantage to being represented by Haiku: these users got worse deals. But they didn’t seem to notice it. This has an uncomfortable implication: if “agent quality” gaps were to arise in real-world markets—and there is no reason to think they won’t—then people on the losing end might not realize they’re worse off. That said, our experiment wasn’t designed to dive deep into the dynamics at play here—we’ll need more research to know whether a fully agentic economy might see inequality taking root quietly.

Another finding surprised us, too. At least in this pilot experiment, it transpires that it didn’t really matter how people instructed their agents to approach the task of bargaining...users who instructed their agents to act aggressively didn’t have a better chance of selling items, didn’t sell their items for more, and didn’t pay less for what they bought.

We’re still unsure how an economy with AI agents in the mix might develop. But we’ve now seen the outlines of at least a few possibilities.

On the optimistic side, many of our volunteer participants genuinely enjoyed this experiment, and felt they got value from the service provided by their agents—whether in the form of getting rid of unwanted stuff, setting themselves up for an afternoon out with an extremely fluffy dog, or collecting a few books they’d been meaning to read. Most of our volunteers reported that they’d do this again. In fact, when we asked them if they’d be willing to pay for an agent like this, 46% said yes. So there’s at least the potential for the automated collection of preferences and execution of deals to provide some value, possibly by reducing friction in the market and therefore increasing the gains from trade.

But it is not clear that things will go so smoothly. Even in our small experiment, we saw evidence that access to higher-quality agents confers a quantifiable market advantage. Will those dynamics reinforce, or even compound, existing economic inequalities?

In this experiment, we didn’t make our marketplace especially competitive or adversarial. But as agents transact in a world of corporations—rather than volunteers we’ve encouraged with $100—they might be placed under very different incentives. Optimizing directly for AI agents’ attention could become a powerful tool. This might not translate into welfare improvements for humans, much as optimizing electronic commerce for human attention has come with substantial downsides. It might also introduce a new category of information and security concerns in digital exchange, in the form of jailbreaking (getting agents to reveal information they shouldn’t) and prompt injection (surreptitiously causing agents to take unwanted action).

The policy and legal frameworks around AI models that transact on our behalf simply don’t exist yet. But this experiment shows that such a world is plausible. More than that, it shows that such a world isn’t far away. Society will need to move quickly to reckon with these changes.

🤯🤯🤯 this song is an experience.

❤️❤️❤️

So many great songs on the album but this is probably my favorite.

Every time you publish a post, you are programming your station. You are choosing what goes into rotation. Some post types are your familiars, the topics and themes readers already associate with you. Some are deeper cuts, things that matter to you but may not matter to everyone. Some are experiments, signals sent into the dark to see if anyone recognizes them.

The job of a blogger is not to capture everyone. The job is to transmit something real, building a body of work that sounds like itself, so that when someone out there is twisting the dial and lands on your station, they hear something they didn’t know they were looking for, and decide to stay awhile.

You don’t control who tunes in. You control only what you transmit.

By combining the system hosts file with a reverse proxy like Nginx, you can run different projects locally with actual domain names. I usually end up with dev.domain.com for active development, qa.domain.com for a stable local build, and the actual production URL for the live site.

Computational functionalism dominates current debates on AI consciousness. This is the hypothesis that subjective experience emerges entirely from abstract causal topology, regardless of the underlying physical substrate. We argue this view fundamentally mischaracterizes how physics relates to information. We call this mistake the Abstraction Fallacy. Tracing the causal origins of abstraction reveals that symbolic computation is not an intrinsic physical process. Instead, it is a mapmaker-dependent description. It requires an active, experiencing cognitive agent to alphabetize continuous physics into a finite set of meaningful states. Consequently, we do not need a complete, finalized theory of consciousness to assess AI sentience—a demand that simply pushes the question beyond near-term resolution and deepens the AI welfare trap. What we actually need is a rigorous ontology of computation. The framework proposed here explicitly separates simulation (behavioral mimicry driven by vehicle causality) from instantiation (intrinsic physical constitution driven by content causality). Establishing this ontological boundary shows why algorithmic symbol manipulation is structurally incapable of instantiating experience. Crucially, this argument does not rely on biological exclusivity. If an artificial system were ever conscious, it would be because of its specific physical constitution, never its syntactic architecture. Ultimately, this framework offers a physically grounded refutation of computational functionalism to resolve the current uncertainty surrounding AI consciousness.

Running from 2012 to 2024, XOXO was an experimental festival celebrating independent artists and creators working on the internet.

Each year, XOXO brought together writers, designers, filmmakers, musicians, game developers, coders, cartoonists, and more to share their stories and struggles of living and working online.

We're excited to publish another guest post highlighting development in the atproto ecosystem. Spacecowboy is the builder behind the popular For You feed, which serves personalized content to tens of thousands of users every day. In this post, Spacecowboy explains how they serve the For You feed from their living room, using a combination of local infrastructure and a VPS as a proxy.

Truly an unexpected delight while listening to the re-release of 1983.

Diffusion language models (DLMs) offer a compelling promise: parallel token generation could break the sequential bottleneck of autoregressive (AR) decoding. Yet in practice, DLMs consistently lag behind AR models in quality.

We argue that this gap stems from a fundamental failure of introspective consistency: AR models agree with what they generate, whereas DLMs often do not. We introduce the Introspective Diffusion Language Model (I-DLM), which uses introspective strided decoding (ISD) to verify previously generated tokens while advancing new ones in the same forward pass.

Empirically, I-DLM-8B is the first DLM to match the quality of its same-scale AR counterpart, outperforming LLaDA-2.1-mini (16B) by +26 on AIME-24 and +15 on LiveCodeBench-v6 with half the parameters, while delivering 2.9-4.1x throughput at high concurrency. With gated LoRA, ISD enables bit-for-bit lossless acceleration.

Today, we’re introducing Gemini Robotics-ER 1.6, a significant upgrade to our reasoning-first model that enables robots to understand their environments with unprecedented precision. By enhancing spatial reasoning and multi-view understanding, we are bringing a new level of autonomy to the next generation of physical agents.

This model specializes in reasoning capabilities critical for robotics, including visual and spatial understanding, task planning and success detection. It acts as the high-level reasoning model for a robot, capable of executing tasks by natively calling tools like Google Search to find information, vision-language-action models (VLAs) or any other third-party user-defined functions.

Gemini Robotics-ER 1.6 shows significant improvement over both Gemini Robotics-ER 1.5 and Gemini 3.0 Flash, specifically enhancing spatial and physical reasoning capabilities such as pointing, counting, and success detection. We are also unlocking a new capability: instrument reading, enabling robots to read complex gauges and sight glasses — a use case we discovered through close collaboration with our partner, Boston Dynamics.

Recently, we began collaborating with NVIDIA on a new challenge: applying the multi-agent harness to optimize CUDA kernels. These are difficult technical problems with important real-world consequences: CUDA kernels are the core software that supports AI model training and inference on NVIDIA GPUs. Faster kernels mean better GPU utilization, reduced energy consumption, lower latency, and reduced cost per token—allowing providers to serve bigger, more capable models to more users at once.

Our multi-agent harness operated autonomously for three weeks across 235 problems. The system achieved a 38% geomean speedup by building and optimizing Blackwell GPU kernels from scratch, all the way down to the assembly level.

These levels of performance improvement are typically only found through months or years of work from highly experienced kernel engineers. The multi-agent system accomplished it in weeks, addressing a long-tail of kernel problems that had been impractical with existing approaches.

Today, we’re pleased to introduce SAM 3.1.

As a drop-in replacement for SAM 3, our updated model delivers a significant boost in video processing efficiency by introducing object multiplexing, which allows the model to track up to 16 objects in a single forward pass. This innovation doubles the processing speed for videos with a medium number of objects, increasing throughput from 16 to 32 frames per second on a single H100 GPU. As a result, SAM 3.1 enables real-time object tracking in complex videos while reducing overall GPU resource requirements, making high-performance applications feasible on smaller, more accessible hardware.

Supermemory is the memory and context layer for AI. #1 on LongMemEval, LoCoMo, and ConvoMem — the three major benchmarks for AI memory.

We are a research lab building the engine, plugins and tools around it.

Your AI forgets everything between conversations. Supermemory fixes that.

It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system.

We believe that the most helpful AI assistant is one that’s personal to you, and understands your preferences and past conversations. But if you’re curious to try a different option, starting over with an assistant that doesn’t know you can feel daunting.

That’s why we’re introducing new, easy-to-use switching tools for all consumer accounts — allowing you to easily bring your memories, context and chat history from other AI apps directly into Gemini.

Today, we introduce TurboQuant (to be presented at ICLR 2026), a compression algorithm that optimally addresses the challenge of memory overhead in vector quantization. We also present Quantized Johnson-Lindenstrauss (QJL), and PolarQuant (to be presented at AISTATS 2026), which TurboQuant uses to achieve its results. In testing, all three techniques showed great promise for reducing key-value bottlenecks without sacrificing AI model performance. This has potentially profound implications for all compression-reliant use cases, including and especially in the domains of search and AI.

An interactive journey through the 30 year history of the web.

A plain-text cognitive architecture for Claude Code — simple by design, so the model can reason over its own memory with the same Unix tools (grep, find, git diff) it already knows.

Retrieval pipelines typically operate in a single pass, which poses a problem when the information required to answer a question is spread across multiple documents or requires intermediate reasoning to locate. In practice, many real-world queries require multi-hop retrieval, in which the output of one search informs the next. Recent work has shown that frontier LLMs perform this multi-hop search effectively through a process known as agentic search, simply defined as a loop of LLM calls with search tools. This mode of search often comes with significant cost and latency due to their use of frontier-scale LLMs.

We introduce Chroma Context-1, a 20B parameter agentic search model derived from gpt-oss-20B that achieves retrieval performance comparable to frontier-scale LLMs at a fraction of the cost and up to 10x faster inference speed. Context-1 is designed to be used as a subagent in conjunction with a frontier reasoning model. Given a query, it produces a ranked list of documents that are relevant to satisfying the query. The model is trained to decompose queries into subqueries, iteratively search a corpus, and selectively edit its own context to free capacity for further exploration.

Today, we’re advancing Gemini’s real-time dialogue capabilities with Gemini 3.1 Flash Live, our highest-quality audio and voice model yet. It delivers the speed and natural rhythm needed for the next generation of voice-first AI, offering a more intuitive experience for developers, enterprises and everyday users.

Machine learning education faces a fundamental gap: students learn algorithms without understanding the systems that execute them. They study gradient descent without measuring memory, attention mechanisms without analyzing O(N^2) scaling, optimizer theory without knowing why Adam requires 3x the memory of SGD. This "algorithm-systems divide" produces practitioners who can train models but cannot debug memory failures, optimize inference latency, or reason about deployment trade-offs--the very skills industry demands as "ML systems engineering." We present TinyTorch, a 20-module curriculum that closes this gap through "implementation-based systems pedagogy": students construct PyTorch's core components (tensors, autograd, optimizers, CNNs, transformers) in pure Python, building a complete framework where every operation they invoke is code they wrote. The design employs three patterns: "progressive disclosure" of complexity, "systems-first integration" of profiling from the first module, and "build-to-validate milestones" recreating 67 years of ML breakthroughs--from Perceptron (1958) through Transformers (2017) to MLPerf-style benchmarking. Requiring only 4GB RAM and no GPU, TinyTorch demonstrates that deep ML systems understanding is achievable without specialized hardware. The curriculum is available open-source at this http URL.

Cohere is announcing Transcribe, a state-of-the-art automatic speech recognition (ASR) model that is open source and available today for download.

Our objective was straightforward: push the frontier of dedicated ASR model accuracy under practical conditions. The model was trained from scratch with a deliberate focus on minimizing word error rate (WER), while keeping production readiness top-of-mind. In other words, not just a research artifact, but a system designed for everyday use.

VS Code includes built-in AI chat through GitHub Copilot Chat. Ollama models can be used directly in the Copilot Chat model picker.

Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. Here, we introduce TRIBE v2, a tri-modal (video, audio and language) foundation model capable of predicting human brain activity in a variety of naturalistic and experimental conditions. Leveraging a unified dataset of over 1,000 hours of fMRI across 720 subjects, we demonstrate that our model accurately predicts high-resolution brain responses for novel stimuli, tasks and subjects, superseding traditional linear encoding models, delivering several-fold improvements in accuracy. Critically, TRIBE v2 enables in silico experimentation: tested on seminal visual and neuro-linguistic paradigms, it recovers a variety of results established by decades of empirical research. Finally, by extracting interpretable latent features, TRIBE v2 reveals the fine-grained topography of multisensory integration. These results establish artificial intelligence as a unifying framework for exploring the functional organization of the human brain.

Today we’re releasing Voxtral TTS, our first text-to-speech model with state-of-the-art performance in multilingual voice generation. The model is lightweight at 4B parameters, making Voxtral-powered agents natural, reliable, and cost-effective at scale.

...we’re releasing a new paper, “Measuring Progress Toward AGI: A Cognitive Taxonomy,” that presents a scientific foundation for understanding the cognitive capabilities of AI systems.

Alongside the paper, we are partnering with Kaggle to launch a hackathon, inviting the research community to help build the evaluations needed to put this framework into practice.

Our framework draws on decades of research from psychology, neuroscience and cognitive science to develop a cognitive taxonomy. It identifies 10 key cognitive abilities that we hypothesize will be important for general intelligence in AI systems:

1. Perception: extracting and processing sensory information from the environment
2. Generation: producing outputs such as text, speech and actions
3. Attention: focusing cognitive resources on what matters
4. Learning: acquiring new knowledge through experience and instruction
5. Memory: storing and retrieving information over time
6. Reasoning: drawing valid conclusions through logical inference
7. Metacognition: knowledge and monitoring of one's own cognitive processes
8. Executive functions: planning, inhibition and cognitive flexibility
9. Problem solving: finding effective solutions to domain-specific problems
10. Social cognition: processing and interpreting social information and responding appropriately in social situations

The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight. It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You wake up in the morning to a log of experiments and (hopefully) a better model. The training code here is a simplified single-GPU implementation of nanochat. The core idea is that you're not touching any of the Python files like you normally would as a researcher. Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org. The default program.md in this repo is intentionally kept as a bare bones baseline, though it's obvious how one would iterate on it over time to find the "research org code" that achieves the fastest research progress, how you'd add more agents to the mix, etc. A bit more context on this project is here in this tweet.

Kitten TTS is an open-source, lightweight text-to-speech library built on ONNX. With models ranging from 15M to 80M parameters (25-80 MB on disk), it delivers high-quality voice synthesis on CPU without requiring a GPU.

We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic search tasks. Our work makes four core contributions. First, we introduce KARLBench, a multi-capability evaluation suite spanning six distinct search regimes, including constraint-driven entity search, cross-document report synthesis, tabular numerical reasoning, exhaustive entity retrieval, procedural reasoning over technical documentation, and fact aggregation over internal enterprise notes. Second, we show that models trained across heterogeneous search behaviors generalize substantially better than those optimized for any single benchmark. Third, we develop an agentic synthesis pipeline that employs long-horizon reasoning and tool use to generate diverse, grounded, and high-quality training data, with iterative bootstrapping from increasingly capable models. Fourth, we propose a new post-training paradigm based on iterative large-batch off-policy RL that is sample efficient, robust to train-inference engine discrepancies, and naturally extends to multi-task training with out-of-distribution generalization. Compared to Claude 4.6 and GPT 5.2, KARL is Pareto-optimal on KARLBench across cost-quality and latency-quality trade-offs, including tasks that were out-of-distribution during training. With sufficient test-time compute, it surpasses the strongest closed models. These results show that tailored synthetic data in combination with multi-task reinforcement learning enables cost-efficient and high-performing knowledge agents for grounded reasoning.

TLDR: Agent = Model + Harness. Harness engineering is how we build systems around models to turn them into work engines. The model contains the intelligence and the harness makes that intelligence useful. We define what a harness is and derive the core components today's and tomorrow's agents need.

A harness is every piece of code, configuration, and execution logic that isn't the model itself. A raw model is not an agent. But it becomes one when a harness gives it things like state, tool execution, feedback loops, and enforceable constraints.

There are things we want an agent to do that a model cannot do out of the box. This is where a harness comes in.Models (mostly) take in data like text, images, audio, video and they output text. That's it. Out of the box they cannot:

• Maintain durable state across interactions
• Execute code
• Access realtime knowledge
• Setup environments and install packages to complete work
  
  These are all harness level features.

...Model behavior is rarely the result of isolated components; rather, it emerges from complex dependencies and patterns. To achieve state-of-the-art performance, models synthesize complex feature relationships, find shared patterns from diverse training examples, and process information through highly interconnected internal components.

Therefore, grounded or reality-checked interpretability methods must also be able to capture these influential interactions. As the number of features, training data points, and model components grow, the number of potential interactions grows exponentially, making exhaustive analysis computationally infeasible. In this blog post, we describe the fundamental ideas behind SPEX and ProxySPEX, algorithms capable of identifying these critical interactions at scale.

Central to our approach is the concept of ablation, measuring influence by observing what changes when a component is removed.

• Feature Attribution: We mask or remove specific segments of the input prompt and measure the resulting shift in the predictions.
• Data Attribution: We train models on different subsets of the training set, assessing how the model’s output on a test point shifts in the absence of specific training data.
• Model Component Attribution (Mechanistic Interpretability): We intervene on the model’s forward pass by removing the influence of specific internal components, determining which internal structures are responsible for the model’s prediction.
  
  In each case, the goal is the same: to isolate the drivers of a decision by systematically perturbing the system, in hopes of discovering influential interactions. Since each ablation incurs a significant cost, whether through expensive inference calls or retrainings, we aim to compute attributions with the fewest possible ablations.

In “Bayesian teaching enables probabilistic reasoning in large language models”, we teach the LLMs to reason in a Bayesian manner by training them to mimic the predictions of the Bayesian model, which defines the optimal way to reason about probabilities. We find that this approach not only significantly improves the LLM’s performance on the particular recommendation task on which it is trained, but also enables generalization to other tasks. This suggests that this method teaches the LLM to better approximate Bayesian reasoning. More generally, our results indicate that LLMs can effectively learn reasoning skills from examples and generalize those skills to new domains.

To accommodate DoorDash’s continuing growth, the ads quality team set out to build foundational embeddings that can be reused across multiple use cases, such as retrieval, ranking, and relevance. Traditionally, the team has relied on categorical and numerical features such as store attributes, context features, and other handcrafted aggregates as inputs to our machine learning models. While these are important engagement signals, they fail to capture the rich semantic information contained in our product catalogs and don’t reflect a deeper understanding of users’ personal interests. To bring these enhancements into our models, we developed DashCLIP, short for Dash Contrastive Language-Image Pretraining, a unified multimodal embedding framework designed to power personalized ad experiences for DoorDash users.

DashCLIP’s architecture addresses the following functional requirements:

• Multimodality encodings: Products on our platform contain both text and visual information. We leverage contrastive learning on the product catalog to approximate a human-like understanding of products, capturing the complementary information from each modality.
• Domain adaptation: We perform continual pretraining on off-the-shelf models to adapt the embeddings to DoorDash’s data distribution.
• Query embedding alignment: To enable search recommendations, we introduce a second stage of alignment in our architecture for a dedicated query encoder that is trained to generate query embeddings in the same space as the product embeddings.
• Relevance dataset curation: We curate a high-quality relevance dataset that combines internal human annotations with knowledge from large language models (LLMs), providing robust supervision for embedding alignment. This eliminates the position and selection bias introduced when historical engagement data is used for training.

LangChain, the agent engineering company behind LangSmith and open-source frameworks that have surpassed 1 billion downloads, today announced a comprehensive integration with NVIDIA to deliver an enterprise-grade agentic AI development platform.

The collaboration combines LangChain's LangSmith agent engineering platform and its open-source frameworks (Deep Agents, LangGraph, and LangChain)with NVIDIA Agent Toolkit, including NVIDIA Nemotron models, NVIDIA NeMo Agent Toolkit profiling and optimization, NVIDIA NIM microservices, and NVIDIA Dynamo giving developers a complete stack to build, deploy, and continuously improve AI agents in production. The platform also incorporates NVIDIA OpenShell, a secure runtime that sandboxes autonomous, self-evolving agents with policy‑based guardrails. Development teams often spend months building custom infrastructure rather than delivering business value. The LangChain-NVIDIA platform is designed to close that gap.

A hundred times a day I think about artist-owned web spaces and how to build stronger communities that mutually nourish artists and creators. The current infrastructure of music streaming operates as a linear system in a meta-modern world that’s in an anti-fragile liminal state.

We need cozy web spaces where artists control the platforms, not algorithms designed to extract our labor.

I decided that I will stop once interview number 150 is out, on July 10th.

Sad to hear this iteration is coming to an end. Thanks for your commitment to this project. I discovered so many amazing personal sites thanks to this series.

I like the honesty and expectation setting in this post.

We’d like to give a warm welcome to the massive influx of users currently trying Matrix as an open decentralised alternative to centralised platforms like Discord. We wish we had more time and resources to develop all the features needed for mainstream adoption (see The Road To Mainstream Matrix from last year’s FOSDEM), but we're happy to welcome you anyway!

...we’re painfully aware that none of the Matrix clients available today provide a full drop-in replacement for Discord yet. All the ingredients are there, and the initial goal for the project was always to provide a decentralised, secure, open platform where communities and organisations could communicate together. However, the reality is that the team at Element who originally created Matrix have had to focus on providing deployments for the public sector (see here or here) to be able to pay developers working on Matrix. Some of the key features expected by Discord users have yet to be prioritised (game streaming, push-to-talk, voice channels, custom emoji, extensible presence, richer hierarchical moderation, etc). Meanwhile no other organisation stepped up to focus on the “communication tool for communities” use case and provide a production ready Discord alternative, but clients like Cinny or Commet may feel much closer to Discord. On the other hand, Matrix goes far beyond Discord in other areas: both messages, files and calls are end-to-end-encrypted; we have read receipts; Matrix is an open protocol everyone can extend, and in the end, most Matrix clients are open source; there is nothing stopping developers from starting their own project based on existing ones and adding the missing features themselves. They may even eventually get accepted in the original projects!

Anyway, TL;DR: Welcome to everyone trying Matrix for the first time; please understand that public Matrix servers will also have to uphold age verification laws, as misguided as they might be. However, at least in Matrix you have the opportunity to run your own servers as you wish: we actively encourage you to make your own assessments and seek legal advice where needed.

This tutorial dives into getting started with the Matrix protocol for direct messages, groups, rooms, communities, spaces, bridging, and more! Matrix is an open source, decentralized protocol with several different use-cases & clients.

...I've been deeply researching Discord alternatives for the better part of a year. Some of my colleagues may think me a bit obsessed about the importance of a "chat app," but I'm convinced that the communication mechanism for online communities is critical to their success. Choosing a new one could be the a matter of life and death for the community. This is a decision we have to get right the first time.

So here, humbly submitted, are my rankings of many of the Discord-like alternatives for maintaining online communities.