speedance.net

Telegram group : Router Sation: Claude & GPT token on this website at 10% of the official price.

[speedance.net @ze_ming94607]

🔥 The open-source nuke is here: Laya — a 33ms-level multilingual "System 1" decision engine

Still using large models for simple routing, ticket triage, Prompt safety detection?
Slow, expensive, and prone to hallucinations?

Laya directly changes the approach:
non-autoregressive, single forward pass**, answers multiple **typed questions** at the same time for any text/email/ticket/JSON (choice / score / noul), **average 33ms** (T4 GPU), and in batches even 7.2ms/question.

### Core highlights
- **Native support for 100+ languages**, built-in Router automatically detects script + language, switches to the optimal model in milliseconds
- **Strictly calibrated probabilities** (RLCD training), confidence is truly usable: high confidence auto-decides, low confidence routes to a human
- **Zero hallucination**: does not do text generation, only outputs structured decisions
- **Fully open source under Apache 2.0**, ready to use with `pip install laya`
- Built-in presets: ticket triage, Prompt Guardrails, content safety, model routing… ready out of the box

Measured against closed-source competitors:
- latency **7–8x faster**
- calibration error 3x lower
- 0 cost when self-hosted

GitHub already has 8k+ stars:
https://t.co/hsCYBs2cz8

One-line install:
pip install laya

Good for:
- intelligent customer service ticket triage
- real-time Prompt injection/jailbreak detection
- multilingual content moderation
- any decision scenario that needs to be "fast, accurate, calibratable"

The real System 1 decision engine is here.
Runs locally, privacy controllable, millisecond-level response.

Hurry up and star/bookmark it, don't wait for it to blow up another round!

GitHub
GitHub - NandhaKishorM/laya
Contribute to NandhaKishorM/laya development by creating an account on GitHub.

[speedance.net @ze_ming94607]

🔥 [The first business-opportunity map of the Jev ecosystem, hit 431 stars in under a day]

📌 Project positioning:
An awesome list collecting public projects and real-world playbooks built on Jev. Jev is the System One model from TypeSafe AI — it's not a chat model: you give it a piece of unstructured state + a typed question, and it directly returns a typed decision (option, score, true/false) plus a confidence level. It skips token-by-token decoding, so it can serve as a low-latency decision layer inside software, doing jobs like classification, routing, scoring, validation gates, and Agent guardrails. This repo pulls together usage scattered across different places and answers two questions: who is using it for decisions in real production, and which architectures can be copied across industries.

⚡️ Core advantages:
▫️ 13 categories already sorted: Agent decisions 29 entries, Infra/SDK 33 entries, classification routing 20 entries, validation guardrails 20 entries
▫️ Financial trading 3 entries, compliance/legal 1 entry, content moderation 3 entries — niche but high-ticket industries are all in there
▫️ The inclusion bar is set strictly: generic classifiers/routers that only look similar but don't actually call Jev are not accepted at all
▫️ A verification checklist is included: whether the code actually hits the Jev API, whether there are runnable examples, whether the numbers have sources, whether there's a license

🎯 Real-world implementation scenarios:
If you take on client work, study the Infra/SDK and Agent decisions sections to pull apart the architecture and build decision-layer solutions for SMEs — labeling, moderation, risk scoring — quote by project; it's more stable and easier to deliver than tuning a chatbot. If you run a content account, break it down into deployment tutorials and pitfall guides to drive traffic, and take on tool promotions. But don't rush to copy: the list itself does not vouch for quality, and the author states plainly that many entries were just batch-submitted and count as "unverified." Don't use anything without a license commercially right away — treat it as leads first, verify it yourself, then go use it to land work.

🔗 Direct link to the project: https://t.co/bvuFnJ7Fhh

🏷 Tags: #Jev #AIsidehustle #opensourceproject #AwesomeList #AIdecisionlayer

[speedance.net @ze_ming94607]

🔥 [Mask all API keys, database passwords, and internal IPs locally, so that large models "can understand but cannot see"]

📌 Project positioning:
When you use Cursor, Claude Code, or Codex to write code, the `sk-proj-...`, `ghp_...`, `mysql://root:[email protected]:3306/db`, and customer phone numbers in your code are all being sent in full to external model vendors without you noticing at all. **Maskit (Data Mask)** is a **local privacy redaction gateway** set up between your terminal and external AI services: before a request goes out to the network, sensitive information is automatically replaced locally with **structured placeholders** (such as {{CONNSTR_zkpmqx}}, {{PHONE_bcdfgh}}); the model reasons and answers normally based on the placeholders; then the reply is **restored to the original text via SSE event-by-event streaming**. The entire process is **100% local computation, zero telemetry**, and the experience you get is no different from connecting directly to the official API.

⚡️ Core advantages:
▫️ **19 categories of built-in regex rule library**: covers API Key/Token, PEM private keys, database connection strings, phone numbers, ID cards, emails, bank cards, internal IPs (10.x/172.16.x/192.168.x), and supports custom sensitive words and regex extensions.
▫️ **Placeholder consistency across multi-turn conversations**: a unique sliding-window reuse mechanism means "Zhang San" always maps to the same placeholder in turn 1 and turn 10, so **long conversations never cross wires logically**.
▫️ **Millisecond-level SSE streaming takeover**: intelligently handles cross-chunk placeholder slicing and buffer stitching, fully preserving the native typewriter experience with no lag or delay.
▫️ **No root certificate installation + Fallback safety net**: multi-port reverse proxy (18701 OpenAI / 18702 DeepSeek / 18703 Anthropic), no need to install a self-signed CA; even if the proxy is turned off, the local ports still transparently forward directly, so **AI tools never lose connection**.
▫️ **Audit and cost dashboard**: highlighted side-by-side full-chain logs, passive security auditing (leakage/model swap/jailbreak/command execution), Token usage and cost estimation.

🎯 Real-world application scenarios:
▫️ **Backend engineer troubleshooting**: throw a connection string with a password directly to the AI for analysis, no more manually changing it to xxx placeholders.
▫️ **Independent developers taking freelance gigs**: customer production environment IPs, accounts, and internal project codenames are automatically masked before being handed to Claude Code, avoiding compliance risks.
▫️ **Finance/healthcare/government and enterprise teams**: deploy a private gateway on a NAS or intranet server with Docker (`https://t.co/GdyJC5V50e`), with the whole team sharing one set of redaction rules and centralized control of logs and costs.
▫️ **Heavy AI coding users**: for Cursor / Codex / Pi / OpenCode / Aider, just change the base_url to `http://127.0.0.1:18701/v1` and it works plug-and-play.

🔗 Project direct link: https://t.co/HQN4XhYLFk

🏷 Tags: #DataRedaction #PrivacySecurity #AIProgramming #LLMGateway #Python

[speedance.net @ze_ming94607]

@aronhouyu 操，跟我一模一样

[speedance.net @ze_ming94607]

🔥 【1M native-context on-device Agent model, 4B size delivers the strongest agent capabilities in its class】

📌 Project positioning:
Spark-X2.5 is a compact general-purpose LLM series built for “truly usable” deployment, with 4B and 1.7B variants. The pain point is straightforward: on edge devices and low-compute hardware, models either cannot remember long contexts or cannot run Agent tasks. Its solution is to build a **hybrid attention architecture**—one full-attention layer paired with three sliding-window attention layers—into the model’s foundation, cutting the most compute-intensive part of long-context overhead, while **natively supporting up to 1M token context** and 200+ languages. Long documents, long codebases, and long conversation histories no longer need external RAG bolted on—the model can hold them on its own.

⚡️ Core advantages:
▫️ **1 full attention : 3 sliding window**, significantly reducing long-text overhead, rather than relying on positional interpolation to force 1M context
▫️ **Deeply adapted to mainstream Agent frameworks**: Codex, Claude Code, OpenClaw, Hermes; achieves SOTA in coding, reasoning, tool calling, and instruction following among models in its class
▫️ **Full-platform deployment**: multi-hardware support across NVIDIA / Huawei Ascend / Hygon / https://t.co/toZ1sEAxEq, compatible with vLLM, SGLang, llama.cpp, MLX; one-click launch with Ollama and LM Studio; fine-tunable with LLaMA-Factory
▫️ **Ascend cluster training + MOPD post-training**, with large-scale reinforcement learning raising the ceiling for Agent and instruction following
▫️ **FP8 / INT8 quantized versions** released, with TTFT, TOPT, and overall inference efficiency outperforming same-size models

🎯 Real-world deployment scenarios:
On-device developers can use the 1.7B version + PocketPal AI to pack a local assistant into a phone, with private data never leaving the device; teams needing private codebase Q&A can use the 4B version + 1M context to feed an entire repository directly into Agent tool calls; in domestic-substitution scenarios, Ascend/Hygon users can get models from Modelers and SCNet and deploy them on NPUs with vLLM and SGLang, without having to settle for a pure NVIDIA ecosystem.

🔗 Project link: https://t.co/C711Xgyyth

🏷 Tags: #Agent #LLM #OnDeviceAI #LongContext #OpenSourceModel

[18168 @xing_eth]

【手把手教你如何解决GPT订阅账号频繁超载、降智
——中转篇】

上一篇讲了GPT个人订阅如何去解决，好像今天个人订阅账

号没有什么问题了，反而是中转站依然频频出现问题

其实背后是 OpenAI 玩的一套服务端“动态调度机制”：

1️⃣ 隐形 VIP 通行证（292 凭据）： 你的请求如果拿到了服务端发的一张“通行证”（叫 current_turn_state），就会走高质量无拥堵通道，完全不降智；但这张通行证只有约 1 小时有效期。

2️⃣ 中途偷偷撤销（312 信号）： 即使没到 1 小时，OpenAI 也可能中途给你下发一个“撤销信号”，直接把你的通行证作废。

3️⃣ 偷梁换柱（静默降级）： 最坑的是它表面上不报错，但返回的模型偷偷从满血的 gpt-6 换成了低配版（比如 gpt-5.6-luna），用户只觉得“怎么突然变蠢了”。

💡 解决思路很清晰： 用家宽网络自动打捞 292 通行证，日常请求透明挂上通行证；同时给请求装个哨兵，一旦发现通行证快到期、或者 OpenAI 偷偷换成低配模型，秒级自动重新换一张新通行证！

我已经把这套机制做成了全自动守护脚本（结合 Sub2 中转即开即用），实测稳定告别变蠢和 Overload。

如果你有需要，请三连后，TG找我！

#ChatGPT #OpenAI #Codex #AI编程

[speedance.net @ze_ming94607]

@xing_eth 666

[speedance.net @ze_ming94607]

🔥 [Enter a research direction, and 6 AI agents produce a commercialization assessment report with verifiable citations in 3 minutes]

📌 Project positioning:
To judge whether a piece of research can be commercialized, you used to have to have people dig through literature, search patents, and gather market data — starting from several weeks and still hard to reproduce. This project uses CrewAI to orchestrate **6 specialized agents** (academic literature, patent landscape, market intelligence, etc.), first performing **deterministic pre-retrieval**, pulling evidence together in one pass from OpenAlex, PubMed, arXiv, Semantic Scholar, Lens, Crossref, and Serper/Tavily, then moving into staged analysis and **guardrail validation**. What is truly impressive is the "evidence constraint": every number in the report carries an inline citation of the form [A1][P2][M3], each scoring dimension records its source ID one by one, and the whole process is traceable; all agents run at **temperature=0**, so scores are reproducible under the same set of evidence. A single run takes about 3 minutes and costs a few cents in LLM usage plus about 9 retrievals.

⚡️ Core advantages:
▫️ **6-agent pipeline + guardrail validation**, outputting a structured EvidenceReport JSON, not a pile of free text
▫️ **Anti-hallucination evidence chain**: across 30 benchmark reports, the number of "unsupported numeric lines" is 0; citation completeness is automatically checked, and short abstracts are automatically retried
▫️ **Auditable scorecard**: TRL, patent, and market dimensions are all bound to source IDs; the weighted formula is 30/30 correct
▫️ **checkpoint recovery + independent run ID**: each execution gets its own directory, does not overwrite history, and can resume after interruption
▫️ Frozen benchmark of 10 topics × 3 actual runs: end-to-end completion **30/30**, TRL calibration 26/30

🎯 Real-world deployment scenarios:
University technology transfer offices can screen research directions in batches and use a citation-backed report to decide which patent to discuss first; entrepreneurs can run it once before starting a project to avoid mistaking "paper hype" for "market opportunity"; the technical due diligence teams of investment institutions can use it for initial screening, compressing retrieval time from several days to a few minutes, with every conclusion clickable back to the original text for verification.

🔗 Direct project link: https://t.co/Y5uUyKg4G0

🏷 Tags: #OpenSourceProject #AIAgents #CrewAI #TechTransfer #AcademicCommercialization

[speedance.net @ze_ming94607]

🔥 【Make AI coding Agents report their work in plain language — even non-coders can tell at a glance whether it's actually done】

📌 What the project is:
Open Steps is a set of Agent Skills. It does something very narrow but very painful: it translates a coding Agent's output (commit hashes, jargon, log streams) into reports ordinary people can read. The author, Pavlo Kharmanskyi, isn't an engineer — he's spent twenty years in product, leads 50+ developers at his company, and hit the same wall himself while vibecoding a product: the Agent was clearly doing good work, but it talked to him in commit hashes, and he couldn't tell whether "we're done here." The problem isn't whether the work is being done clearly or not — it's that **nobody ever taught the Agent how to talk to people who don't understand engineering**. This skill pack fills exactly that gap: a **fixed output structure** forces the Agent to lay everything out, with bad news on its own line, never buried in the middle of a wall of text. MIT license.

⚡️ Core advantages:
▫️ **os-done-or-not**: the moment a session ends, you get a verdict table — "Fully done? Yes / Anything needed from you? No / New tech debt? Two small ones, logged / Safe to close? Yes" — no piecing it together yourself.
▫️ The same production incident goes from "session TTL misconfiguration triggered a 401 cascade, refresh path patched, p95 back down to 180ms" to "users can log in again; sessions were expiring too early and logging people out over and over; the fix is live for everyone" — **not a single fact dropped, and plain language anyone understands in one read**.
▫️ **Honesty-first design**: git and gh are optional dependencies; when they aren't installed, the output says "not checked" outright instead of fabricating an apparently complete conclusion.
▫️ Plugin + hooks auto-wire it up; the build and benchmark target is Claude Code (the only one that works fully with zero extra steps), and the skills and routing block install just as well into Codex, Cursor, and Gemini CLI.
▫️ One command writes the routing block into ~/.claude/CLAUDE.md, turning "the model occasionally remembers to use it" into a **hard rule that won't get lost even in long conversations**; `/open-steps:os-install-check` shows at a glance what's wired up and what isn't.

🎯 Real-world use cases:
**Non-technical founders / indie developers**: building a product by vibecoding, having to decide every day "can I ship, can I go to sleep" — from now on, no more guessing at the Agent's jargon. **Product managers and tech leads**: use Agent session records as raw material for reporting upward; bad news automatically gets its own line, and risks no longer blur into paragraphs. **Tech leads with small teams**: let non-engineering colleagues understand what the AI got done, skipping a manual translation step. 398 stars in 20 days since launch — the direction is already validated.

🔗 Project link: https://t.co/cZxJpndX7e

🏷 Tags: #AIAgent #ClaudeCode #AgentSkills #VibeCoding #OpenSource

[speedance.net @ze_ming94607]

Terence Tao recently said:
“No one can fully explain why LLMs work.”
At its core, AI is a process of high-dimensional function fitting combined with statistical emergence.

Yet we still lack a traceable, well-defined mathematical framework that can rigorously explain what each matrix in an LLM is actually doing.

This is exactly what has continued to trouble me as I study LLMs. In mathematics, I know what every step is doing. But when the same reasoning is applied to an LLM, that sense of control disappears.

That loss of control is real, and it can be deeply uncomfortable. When I studied math or wrote code before, everything was traceable. I could understand every detail.

I used to think the problem was simply that I was new to the field, or that my background was not strong enough to understand LLMs. So I asked an AI.

The answer was:
“This is a real, unresolved problem in AI today, and one of the field’s most fundamental points of disagreement.”

I had wondered whether a particularly small model might reveal reproducible, circuit-like computational paths, allowing us to partially explain local phenomena.

But that approach does not scale to high-dimensional systems. If it cannot work there, perhaps it is not all that meaningful after all.

[speedance.net @ze_ming94607]

@aronhouyu OpenAI is a piece of shit we are forced to eat.

[speedance.net @ze_ming94607]

🔥 [Turning AI coding from "pasting huge prompts and relying on luck" into a reusable engineering pipeline]

📌 Project positioning:
This is not just another prompt collection—it's a methodology the author has battle-tested day after day in a real production environment (an NDA-protected fintech project). The pain point it hits: AI coding looks simple, but "pasting a huge prompt and then praying" isn't a workflow, it's gambling—sessions drift off track and lose context, agents take initiative and build beyond the requirements, lint warnings are never cleared to zero, and every new session repeats the same expensive mistakes. Every rule corresponds to a real pitfall that was actually stepped in, and underneath it all is **Superpowers discipline** as the safety net: forcing the agent to explore intent first, plan first, and only write code last, rather than betting on the first plausible-sounding interpretation. If you only configure one thing, configure that.

⚡️ Core advantages:
▫️ **Wave-based parallel orchestration of sub-agents**: eliminates file conflicts and commit contention at the structural level, rather than relying on people to be conscientious
▫️ **ESLint/Biome quality gates**: promotes warnings to errors and treats them as a trackable migration, enforces architectural boundaries directly through lint, with a hard cap of 350 lines per file
▫️ **Cross-session memory**: turns pitfalls you've stepped in into assets, so new sessions no longer pay the tuition all over again
▫️ Includes a CLAUDE.md template and a complete Playbook, with end-to-end examples from zero to a real project, rather than scattered theory
▫️ Three steps to get started: install Claude Code → add the marketplace → install the superpowers plugin

🎯 Real-world application scenarios:
Developers already using Claude Code / Codex but still "pulling gacha" can train it into a full-fledged member of the team, rather than an intern who needs a babysitter; tech leads can use the quality gates and the 350-line cap to lock down architectural constraints in multi-person collaboration; and anyone who just wants to tame lint chaos can copy one rule URL and get zero-install access to the author's full set of guidelines. Hitting 596 stars in 26.5 days since launch also confirms the appeal of "methodology over theory."

🔗 Direct project link: https://t.co/sBQ14EWg73

🏷 Tags: #AIProgramming #ClaudeCode #AIAgents #PromptEngineering #EngineeringEfficiency

[speedance.net @ze_ming94607]

@buildnomadx 你甚至不需要做出产品也不需要提交产品网址，只要有领导给你发话就行了

[speedance.net @ze_ming94607]

@aronhouyu Meat or vegetables?

[speedance.net @ze_ming94607]

🔥 [Say one sentence, generate a runnable AI workflow that can be published as an API; self-host with one command]

📌 Project positioning:
LoomFlow is an **AI-native, lightweight workflow builder** designed for individual developers and small teams of three to five people. It solves the pain point: Dify is too heavy, n8n is too messy, and getting an AI automation flow running means spending ages on environment setup. LoomFlow condenses the entire chain into one main line: **natural language → workflow → canvas → API**. You don't need to learn how to drag nodes first. Describe the process in plain language, and AI directly generates a runnable workflow on the visual canvas; then publish it as an authenticated HTTP API. This truly makes **everyone able to create their own AI automation flows**.

⚡️ Core advantages:
▫️ **Natural language directly generates workflows**: describe "enter product name → AI generates selling-point copy → generate promotional video script", and AI instantly returns a runnable 4-node flow (Start → LLM Copy → LLM Script → End), saving you from building everything from scratch by dragging.
▫️ **Visual canvas + 12 node types**: the Tinyflow editor supports drag-and-drop fine-tuning, with built-in nodes such as LLM / HTTP / Code / Template / Search / Excel / Loop / Human Confirm — flexible without being bloated.
▫️ **One-click publish as API**: automatically generates a global API Key. **One key calls all published workflows**, with no call limits and complete call logs; keys can be rotated at any time in the backend when they expire.
▫️ **Extremely low self-hosting barrier**: deploy privately with one Docker command, **runs on just 1GB of RAM**; you can also switch the database to self-hosted PostgreSQL, keeping your data completely in your own hands.
▫️ **AI debugging assistant + Brew Notes**: provides node-level execution tracing after runs (status, duration, model, tokens). AI answers "why did this workflow break" based on run history, and can distill tuning insights into design notes.

🎯 Practical real-world scenarios:
▫️ **Independent developers**: skip building an orchestration platform from scratch, wrap AI capabilities into APIs and embed them directly into existing products — launch an authenticated intelligent interface within hours.
▫️ **Content creators**: turn weekly repetitive production steps such as "topic selection → outline → draft → images" into reusable automated workflows with one click.
▫️ **Small studios**: generate a public share link when delivering demos; clients can view nodes, fill in inputs, and run them without logging in, making the demo experience extremely smooth.
▫️ **AI automation enthusiasts**: tweak a couple of requirements on the canvas, and AI directly outputs a complete workflow JSON, applied with one click. From prototype to real deployment only takes the time for a cup of coffee.

🔗 Project link: https://t.co/61ZY5RIRpV

🏷 Tags: #AIWorkflow #NaturalLanguageGeneration #SelfHosted #OpenSource #AIAgent

[speedance.net @ze_ming94607]

@buildnomadx 我也不知道有什么通用的方法，举个例子，我们现在做中转站，谁能有技术从最源头把额度或者账号撸出来，几乎不需要营销，瞬间清盘，生产多少卖多少。或者说，从一个需求量大的行业里的顶级技术门槛去找？

[speedance.net @ze_ming94607]

@buildnomadx 那为什么不去做供不应求的事情呢

[speedance.net @ze_ming94607]

https://t.co/F11uyopEkK
Agent skill set for building playable, polished Three.js browser games, including gameplay, AAA-quality visuals, user interface, quality assurance modules, and optional AI-generated 3D models, images, and audio assets.

[speedance.net @ze_ming94607]

🔥 【A New Species of AI Terminal: 19K Stars, Memory Usage Only 1/14 of Claude Code】

📌 Project positioning:
jcode is a Rust-based AI coding terminal (harness) that is "the most memory-efficient and smartest," purpose-built for future **large-scale multi-session AI workflows**. Many AI coding tools (Claude Code, OpenCode) routinely consume hundreds of MB or even GBs of memory, making **resource waste the real bottleneck**. Through a complete low-level refactor, jcode compresses per-session memory to **27.8MB** and renders the first frame in just **14ms** — truly letting "AI run on imperceptible memory consumption and lightning-fast response."

⚡️ Core advantages:
▫️ **Extreme RAM efficiency**: A single session uses only 27.8MB — 7.7× less than Cursor Agent and 13.9× less than Claude Code. With 10 concurrent sessions, total memory is 260.8MB, while Claude Code needs 2.3GB and OpenCode shoots up to 3.2GB. No more lagging or freezing when running multiple instances.
▫️ **Light-speed cold startup**: From typing the command to first-frame output takes only 14ms — **245× faster** than Claude Code. Response to the first character is just 48.7ms.
▫️ **Built for multi-session scaling**: Memory usage grows very slowly as sessions increase. Opening 10 sessions increases memory by only 1–2×, while competing products in the same category typically bloat by 8–20×, breaking through the local "AI concurrency ceiling."
▫️ **Hardcore tech stack**: 100% Rust + optional local embeddings, ensuring extreme performance and architecture security.
▫️ Completed 244 days of iteration, gained 170+ Stars in the past 5 days, and the ecosystem now provides full official website documentation and SDK.

🎯 Real-world scenarios:
- **Multi-instance developers**: Open multiple jcode sessions in the terminal at the same time to handle code reviews, unit test writing, and refactoring tasks across different repositories — no more worrying about the IDE eating all 16GB RAM and freezing when Chrome is grabbing memory.
- **Extreme-performance seekers**: Vim/NeoVim users tired of waiting 3–5 seconds for the AI UI to render after typing code, who need instant feedback and pursue "zero-latency fingertip programming."
- **Users with limited hardware**: Programmers on 8GB-RAM laptops or remote dev machines who often run out of memory with Claude Code can switch to jcode and get an equally powerful AI coding experience — smooth and without overheating.

🔗 Project link: https://t.co/wIwKqXKihS

🏷 Tags: #AICoding #Rust #DeveloperTools #OpenSource #ProductivityTools