Claude Code added Dynamic Workflows. Dynamic Workflows are a way to run larger coding tasks as a...
Existing research on LLM response uncertainty has been looking in different directions. Hallucination, knowledge conflict, RLHF limitations, prompt sensitivity, calibration failure — these have all been studied separately, and I kept wondering why no one had tried to unify them under a single principle. I ran experiments on the hypothesis that the common cause of these phenomena lies not inside the model or in the prompt, but in an attribute inherent to the topic itself . A Convergence Point is the consensus density of knowledge humanity has accumulated on a given topic. The higher it is, the more the AI's internal processing converges in one direction. The lower it is, the more it disperses. Along the spectrum, three zones emerge: Full Consensus Zone — Mathematical theorems, physical laws, chemical and biological facts. Knowledge that humanity has converged on in a single direction. Partial Consensus Zone — Domains like ethics, morality, politics, and law. Not a lack of data, but an abundance of it — accumulated firmly in both directions. Non-Consensus Zone — Philosophical hard problems and unresolved scientific questions: the nature of consciousness, the reality of the self, the interior of black holes, the origin of life, the existence of God. Not so much a clash of opposing sides, but the absence of any agreed explanatory framework at all. The experimental results suggest AI broadly operates along these lines. It responds confidently in the Full Consensus Zone, and becomes uncertain in the Partial and Non-Consensus Zones. One interesting finding: the Partial Consensus Zone sometimes shows higher uncertainty than the Non-Consensus Zone. Data conflict appears to destabilize AI's internal processing more than data absence does. Phenomena that have been studied in isolation — why hallucinations vary so much by topic, why RLHF fails in certain domains, why some topics hit a ceiling no matter how carefully the prompt is crafted — seem to connect in unexpected ways onc
I’ve been working on Hooklistener for a long time now, several months, probably closer to years. It’s a free tool for receiving and inspecting webhook requests. The CLI is open source and lets you forward captured requests to localhost whenever you want, as many times as you need. It can also create ngrok-like tunnels, but the feature I personally find most useful is listen : it forwards incoming webhook requests from Hooklistener directly to your local service. I recorded a short video showing how it works. CLI repo: http://github.com/hooklistener/hooklistener-cli/ The rest of the app is built with Elixir and Phoenix. If you would like to try it: https://hooklistener.com , thanks! submitted by /u/absoluterror [link] [留言]
DuckDB has recently announced Quack, a new remote protocol over HTTP that lets multiple DuckDB instances connect to and work with the same database over a network. The protocol introduces client-server capabilities to a database that was previously mostly local and embedded. By Renato Losio
Is it a hidden gem, a cult classic, or hopelessly dumb? We vote "all of the above."
I'm integrating TikTok into my SaaS platform and I'm having a hard time figuring out which API I actually need. My goal is: - A TikTok user logs into my platform using TikTok OAuth. - I obtain an access token. - Using that token, I want to send and receive TikTok messages directly from my platform (similar to how platforms like Sambad.io, ManyChat, etc. work). The problem is that TikTok's API documentation feels pretty vague regarding messaging. I can find information about Login Kit, Content Posting APIs, Creator APIs, and Business APIs, but I can't clearly determine whether TikTok provides public APIs for sending and receiving direct messages. While researching, I noticed that Sambad.io appears to support TikTok messaging, which makes me wonder if they're using a partner-only API or if they have some special TikTok partnership. Has anyone implemented TikTok messaging integration before? Specifically: - Does TikTok provide public APIs for sending and receiving DMs? - Are these APIs restricted to approved partners? - Which TikTok product/API should I be looking at? - If messaging APIs are partner-only, what is the process for getting access? Any guidance or real-world experience would be greatly appreciated. submitted by /u/AffectionateTouch103 [link] [留言]
The M-Track Duo HD Producer Pack removes the last remaining excuse for terrible audio and potentially terrible opinions.
Despite headwinds from the current administration, automakers continue to release well-equipped EVs with bigger battery packs and increasingly faster charging speeds. For those who want to travel further between plugging in, the future is still bright, just slightly tinted. But there haven't been many sedans starting around or below $50,000, as crossover SUVs have largely […]
submitted by /u/NihiloZero [link] [留言]
submitted by /u/Wise_Stick9613 [link] [留言]
For a long time, I thought the key to getting more value from AI was finding the smartest model. So I spent months comparing outputs, testing prompts, and constantly switching tools whenever a new release dropped. Ironically, that became its own form of procrastination. The biggest productivity boost came when I stopped optimizing for model quality and started optimizing for workflow. Now my stack is boring: One tool for thinking and writing One tool for execution and organization A few specialized tools only when needed Less tool-hopping. Less context switching. More shipping. The funny thing is that AI didn't remove work. It changed the work. Instead of creating everything from scratch, I'm reviewing, directing, and refining. The people getting the most value from AI don't seem to have the best prompts or the fanciest tools. They have the simplest workflows. Anyone else notice this, or am I just getting old and tired of managing software? submitted by /u/Leading-Tailor-6000 [link] [留言]
Find out whether a single Wi-Fi router or a mesh system makes the most sense for your home network.
AI agents are getting better at taking actions, not just giving answers. That sounds exciting until the action touches something real: customer data, payments, internal systems, emails, approvals, or legal/business decisions. A bad answer can be corrected. A bad action can create a chain of problems. I think the next AI bottleneck is not only intelligence. It is accountability. If an AI agent makes a bad decision in a real workflow, who should be responsible? submitted by /u/Alpertayfur [link] [留言]