monty

Looking for a iOS and Android mobile app developer to help develop an initial version of an APP for an OTA. Native is preferable but @r_FlutterDev is acceptable as long as nothing is “lost” in the process. - Previous experience connecting to realtime backends like WebSockets is required. - Previous experience with @dotnet @SignalR is a plus. - Previous experience with Tourism industry (in particular OTAs) is a plus. - Be able to work close to Brazilian Timezone (GMT-3) at least part of the day is required. Interested? Please DM me with your resume and/or app portfolio. Scope, technical docs and Figma design will be shared with developers which match the profile. Thanks!

@beijihu client for open source NATS server. NATS is a lightweight messaging server (like Kafka or RabbitMQ but way easier to install and run) ~20 mb binary full features, can be clustered etc.

@_mtmk 这个是干什么用的？

NATS .NET 3.0 is now stable github.com/nats-io/nats.n…

@aSteveCleary oh my! i wrote too many little remove bom scripts. i still don't know of any practical use for it

It's Monday. I have written "new UTF8Encoding(encoderShouldEmitUTF8Identifier: false)" way too many times in my life. How about you?

📣 NATS .NET 3.x preview 9 release is out (we will one last preview before stable) github.com/nats-io/nats.n…

WDYT of a new `NATS.Client.OpenTelemetry` package? looks like it will offer very little benefit but might be convenient? github.com/nats-io/nats.n…

Hope to see everyone tomorrow! synadia.com/lp/rethinkconn…

⏰ One hour to RethinkConn. If you registered, check your inbox for the join link. If you didn't — you can still register and drop in: #register" target="_blank" rel="nofollow noopener">synadia.com/lp/rethinkconn… First session kicks off at 9 AM PT / 12 PM ET. See you there.

OpenTelemetry feedback would be greatly appreciated on the new NATS.Client.OpenTelemetry package proposal github.com/nats-io/nats.n…

Adding OpenTelemetry metric support to NATS .NET 3.0 try the `Example.OpenTelemetry` project in PR branch github.com/nats-io/nats.n…

@marcgravell @JetBrainsRider i had the same issue. i think i edited some xml file under .idea folder to remove them

Gah! I accidentally opened @JetBrainsRider in my "git worktree" parent folder, and now it won't stop showing me deltas from all the branches at once, and I can't get it to stop and go back to the single branch! Have cleaned everything I can think of. Any ideas?

watch it! auto mode in action

c isn’t ugly at all it’s insanely simple and beautiful

oh no!

📣 New NATS .NET releases v2.8.0-preview.3 and v3.0.0-preview.5 are out: 🌟 NATS Server v2.14 support, see release notes 🛠️ Dispose improvement, drain on dispose (opt-in) Please test this if you can github.com/nats-io/nats.n…

really excited about this release 👇

just found out about claude code ide integration

The @synadia team is on a roll. Amazing work on the release of @nats_io server version 2.14. Congrats to the team! docs.nats.io/release-notes/…

If you don't understand this, you will not understand why LLM-based agents are irreparably failing for a general-purpose problem solving. An agent (by the way it was the topic of my PhD 20 years ago) to be useful, must be rational. Being rational means to always prefer an outcome that results in the maximal expected utility to its master/user. Let’s say an agent has two actions they can execute in an environment: a_1 and a_2. If the agent can predict that a_1 gives its user an expected utility of 10, and a_2 gives an expected utility of -100, then a rational agent must choose a_1 even if choosing a_2 seems like a better option when explained in words. The numbers 10 and -100 can be obtained by summing the products of all possible outcomes for each action and their likelihoods. Now here is the problem with LLM-based agents. The LLM is not optimizing expected utility in the environment. It is optimizing the next token, conditioned on a prompt, a context window, and a training distribution full of examples of what helpful answers are supposed to look like. Those are not the same objective. So when we wrap an LLM in a loop and call it an “agent,” we have not created a rational decision-maker. We have created a text generator that can imitate the surface form of deliberation. It may say things like: “I should compare the expected outcomes.” “The best action is probably a_1.” “I will now execute the optimal plan.” But the internal mechanism is not selecting actions by maximizing the user’s expected utility. It is generating a continuation that is statistically appropriate given the prompt and prior context. This distinction matters enormously. For narrow tasks, the imitation can be good enough. If the environment is constrained, the actions are simple, and the success criteria are close to patterns seen in training, the system can appear agentic. But for general-purpose problem solving, the gap becomes fatal. A rational agent needs stable preferences, calibrated beliefs, causal models of the world, the ability to evaluate consequences, and the discipline to choose the action with maximal expected utility even when that action is boring, non-linguistic, or unlike the examples in its training data. An LLM-based agent has none of that by default. It has fluency. It has pattern completion. It has a remarkable ability to compress and recombine human text. But fluency is not rationality, and a plausible plan is not an expected-utility calculation. This is why these systems so often fail in strange, brittle, and irreparable ways when given open-ended responsibility. They are not failing because the prompts are insufficiently clever. They are failing because we are asking a simulator of rational agency to be a rational agent.