标签：#ia

submitted by /u/NihiloZero [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]

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]

submitted by /u/Early_Mail9268 [link] [留言]

I used these two prompts on all AI apps to figure out who to vote for in the CA primaries: If you were running for governor of California, what will your big policies be ⁠Out of the candidates that are running in June election, who aligns closest to those policies Gemini, claude, chatgpt all ranked Matt Mahan (Democrat) as #1 Grok chose Steve Hilton (Republican) thoughts on AI use for voting decisions? submitted by /u/No_Mall_7299 [link] [留言]

Most robot foundation model demos are hard to interpret because the impressive number usually comes after task-specific fine tuning. Wall-OSS-0.5, a new open-source VLA release from X Square Robot, is interesting because the report tries to measure what the pretrained checkpoint can do before that extra adaptation step. The setup is a 4B vision-language-action model built around a 3B VLM backbone plus action-generation components. According to the report, the pretrained checkpoint was evaluated on a 17-task real-robot suite without task-specific fine tuning. Four tasks crossed 80 task progress: block sorting, fruit sorting, ring stacking, and a held-out deformable task, rope tightening. The part that seems more important than the raw score is the framing. In language models, nobody would accept only a fine-tuned downstream score as evidence that pretraining worked. With robots, that has been much harder because the evaluation is physical, slow, embodiment-dependent, and expensive. A real-robot zero-shot suite is a useful step toward asking the same question directly: does pretraining itself produce executable behavior, or is it mostly a better initialization? The method is also trying to solve a specific training problem. Continuous action losses are useful for execution, but the paper argues they do not send a strong enough learning signal into the VLM backbone by themselves. Their recipe combines action-token cross entropy, multimodal cross entropy, and flow matching in one stage, using the discrete action-token path as a gradient bridge into the backbone while flow matching handles continuous actions at deployment time. For reference, the code is at https://github.com/X-Square-Robot/wall-x , the paper is at https://x2robot.com/api/files/file/wall_oss_05.pdf , the project page is https://x2robot.com/oss#resources , and the Hugging Face org is https://huggingface.co/x-square-robot . The caveat is obvious but important. Zero-shot still does not solve the hardest man

CSS @function - DEV Community CSS just got its biggest quality-of-life upgrade since custom properties. For years, if you wanted... dev.to

🔮 Hermes Agent 🤖: A Practical Guide 🔥 — and How It Stacks Up Against OpenClaw & GoClaw 📊 Hermes Agent Challenge Submission Truong Phung Truong Phung Truong Phung Follow May 18 🔮 Hermes Agent 🤖: A Practical Guide 🔥 — and How It Stacks Up Against OpenClaw & GoClaw 📊 # hermesagentchallenge # devchallenge # agents 7 reactions Comments Add Comment 16 min read

For those who cant afford claude models and wanna use claude code, deepseek v4 pro is closest best and cheapest option. How to use deepseek API inside claude code (easist way ever): We will use AI to replace AI. Just feed your existing claude code this prompt "Yo Claude, you’re expensive af 💀 Do everything needed to fully switch Claude Code to DeepSeek API automatically. Set up the complete settings.json config, API integration, model selection, base URL, env variables, testing, debugging, and optimization for low cost + strong coding performance. Use this DeepSeek API key: "sh......................" Make it fully working, minimal, and production ready." Thats it! Thank me later! submitted by /u/Agreeable-Pen-9763 [link] [留言]

Since AI is a top-grossing buzzword for all students , employers , HRs , managers , scientists , engineers and analysts and all other people working in IT industry and other enterprises accross the Earth from early 2024 to till date. What do you all think about this digital transition of the human world present right now and what are the expectations of tommorow ? submitted by /u/Hzrshx [link] [留言]

Hai Guyz, Ive used many apps and it seems the market is flooded with crappy ones. So far my favorites are HiWaifu and Privee. HiWaifu is by far the best text, it doesn't allow sexy pics, just pg-13 ones. Privee has sexy pics but both the text and pics are limited. Another thing Ive noticed with a lot of AI generators is that by default everyone seems to be east Asian. Why is this? submitted by /u/Jiggalopuffii [link] [留言]

I randomly came across this and honestly I can’t tell if it’s real or one of those AI demos that looks impressive but doesn’t actually work. From what I understand, it’s claiming you can fine-tune models, do image training, test them in a playground, and deploy them as an API from a phone. That sounds a little too convenient, which is why I’m skeptical. I haven’t tried it myself yet, but I’m curious if anyone here has. submitted by /u/Raman606surrey [link] [留言]

You hit "Submit Order" and nothing happens. The spinner just spins. Is it processing? Did the request get lost? You click again. If the API on the other end does not implement idempotency, you just placed two orders. Maybe two charges to your card. This is a solved problem — and the solution is simpler than you think. What Is Idempotency? An operation is idempotent if doing it multiple times produces the same result as doing it once. GET requests are naturally idempotent — fetching a resource does not change it. DELETE is also idempotent in practice. The trouble is POST and PATCH : create an order twice, and you get two orders. An idempotency key is a client-generated unique identifier (usually a UUID) that you send with a mutating request. The server stores this key with the result. If the same key arrives again — whether due to a retry, a network blip, or an impatient user — the server returns the cached result instead of executing the operation again. Implementing Idempotency on the Server Here is a minimal Express implementation backed by Redis: const express = require ( " express " ); const redis = require ( " ioredis " ); const { v4 : uuidv4 } = require ( " uuid " ); const app = express (); const cache = new redis (); app . use ( express . json ()); // TTL for idempotency records: 24 hours const IDEMPOTENCY_TTL = 86400 ; async function idempotencyMiddleware ( req , res , next ) { const key = req . headers [ " idempotency-key " ]; if ( ! key ) return next (); // optional on GET/DELETE const cached = await cache . get ( `idem: ${ key } ` ); if ( cached ) { const { status , body } = JSON . parse ( cached ); return res . status ( status ). json ( body ); } // Intercept the response to cache it const originalJson = res . json . bind ( res ); res . json = async ( body ) => { if ( res . statusCode < 500 ) { await cache . setex ( `idem: ${ key } ` , IDEMPOTENCY_TTL , JSON . stringify ({ status : res . statusCode , body }) ); } return originalJson ( body ); }; next ();

INTRODUCTION Configuring a Power BI semantic model involves refining data structures, creating relationships, and setting up calculations. Semantic model is the last stop in the data pipeline before reports and dashboards are built. It is the end product of the raw data that has been extracted, transformed, loaded, modeled, built relationship, and written calculation. The Semantic model consist of Data connections to one or more data sources, Transformations that clean and prepare the data for reporting, Defined calculations and metrics based on business rules to ensure consistent reports and Defined relationships between tables. Key words to note in Semantic Modelling are; 1. Fact table and Dimension table: The Fact table records the quantitative and numerical data. It is where every single details are recorded. The Dimension table act as the descriptive companion to the fact table, containing the attributes or characteristics that provide context to the data. 2. Primary and Foreign Key: Primary Keys are unique identifier assigned to a specific record with a database table ensuring that no two rows are identical or repeated. foreign Keys are columns or group of columns in one table that provides a link between data in two tables by referencing the primary key of another. 3. Star Schema Star Schema is a data modeling technique where a central fact table is surrounded by several dimension tables that provide descriptive content. 4. Cardinality Cardinality defines the kind of relationship between two tables. They are; One to Many (1.*) Many to one (*.1) One to One (1.1) Many to Many ( . ) The cardinality of a relationship is described by the "one" (1) or "many" (*) icons located at the ends of the relationship line. 5. Cross Filter Direction The direction determine how filters propagate. Possible cross filter options are dependent on the relationship cardinality type. One to Many - Single or Both sides One to One - Both sides Many to Many - Single to either table or b

Lightweight local coding agent with emphasis on subagenting rather than stuffing everything into one giant context. The idea is to reduce context rot and kv cache size so as to scale to larger coding tasks using focused parallel workers. submitted by /u/Turbulent-Guest154 [link] [留言]

Sequel to: Learning to Skip Blocks: Self-Discovered Ultrametric Routing for Hardware-Accelerated Sparse Attention Abstract We present Llama Surgery , a method for injecting learned block-sparse attention topologies into pre-trained dense language models without retraining from scratch, distillation, or post-hoc pruning. Starting from a frozen Llama 3.1 8B, we surgically replace each attention layer with a Dynamic Topology Router that maps token embeddings onto the branches of a Bruhat-Tits p-adic tree via factorized Gumbel-Softmax routing. A Continuous Logit Homotopy guarantees that at initialization the injected topology bias is identically zero, preserving the pre-trained manifold exactly. Over training, temperature annealing polarizes the soft routing assignments into hard binary masks, and a Switch Transformer-style load-balancing loss prevents routing collapse. We identify and resolve two critical failure modes: (1) gradient collapse through discrete masking operations, solved by a Straight-Through Estimator bridge that decouples the hard forward mask from the soft backward gradient; and (2) Attention Sink instability, where hard-masking the initial token causes softmax entropy collapse and syntactic degeneration, solved by permanently anchoring Token 0 in the visibility set. The resulting architecture is validated on Llama 3.1 8B fine-tuned on WikiText-2, achieving stable convergence and producing coherent, mathematically sophisticated text while maintaining dynamic block-sparse routing across all 32 transformer layers. A custom Triton forward kernel with Attention Sink and Local Window support, pipelined for Ampere and Hopper architectures ( num_warps=4 , num_stages=3 ), executes the block-sparse prefill phase at O(N) theoretical complexity. To our knowledge, this is the first demonstration of differentiable ultrametric topology injection into a production-scale pre-trained LLM. https://github.com/sneed-and-feed/adelic-spectral-zeta/blob/main/papers/llama_sur

My understanding is that AI won’t do anything if we don’t ask him something, so i was wondering what will happen to AI if no one ask him to do anything. submitted by /u/mansithole6 [link] [留言]

submitted by /u/Hot-Upstairs9603 [link] [留言]

one of the most annoying problems when building AI agents: fix a failure, change something, same failure comes back quietly. built replayd for this. captures failed runs as regression tests and replays them before you ship. catches the failure if it returns after a prompt, model, or tool change. v0.1.2, pip installable, open source. pip install replayd star it if you want to follow progress. submitted by /u/taimoorkhan10 [link] [留言]

Counted it last week: one monday review had me opening 6 apps and copy-pasting between all of them, while a chatbot sat in a 7th tab handing me summaries i still had to go act on. that's the part the 'ai is useless' crowd is actually right about. text out, the work is still on you. what moved me off that take wasn't a smarter model. it was dropping the chat window for a desktop agent that reads gmail, calendar and slack inside the same task and takes the next step itself, with a permission prompt before each action so it isn't running wild. the $500m-wasted-on-claude thread up top is the same thing from the money side. paying for tokens that spit out paragraphs nobody executes is just the expensive way to do nothing. If you're still in the 'it doesn't actually do anything' camp, fair, i was there too. the line for me was the day it finished a task instead of describing one. written with ai submitted by /u/Deep_Ad1959 [link] [留言]