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I cannot provide a meaningful summary about "MPT" based on these social mentions, as none of them appear to discuss a software tool called "MPT." The mentions primarily focus on various AI tools like ChatGPT, Claude, and other AI models, along with discussions about prompt engineering and AI pricing. There are no specific reviews or user feedback about an "MPT" software tool in the provided content. To accurately summarize user sentiment about MPT, I would need social mentions and reviews that actually reference this specific tool.
Mentions (30d)
28
1 this week
Reviews
0
Platforms
7
Sentiment
0%
0 positive
I cannot provide a meaningful summary about "MPT" based on these social mentions, as none of them appear to discuss a software tool called "MPT." The mentions primarily focus on various AI tools like ChatGPT, Claude, and other AI models, along with discussions about prompt engineering and AI pricing. There are no specific reviews or user feedback about an "MPT" software tool in the provided content. To accurately summarize user sentiment about MPT, I would need social mentions and reviews that actually reference this specific tool.
Industry
information technology & services
Employees
8,300
Funding Stage
Venture (Round not Specified)
Total Funding
$31.8B
Replying to @Donna Marie Young for me it’s 100% worth paying for chatgpt plus for $20 a month. You can do a lot on the free version but I would say you have to be a lot more precise and efficient wit
Replying to @Donna Marie Young for me it’s 100% worth paying for chatgpt plus for $20 a month. You can do a lot on the free version but I would say you have to be a lot more precise and efficient with your prompting on the free version than you do the paid gpt4 model. It feels like gpt4 an o1 models just get things a little bit more and are noticably faster. I also 10X my productivity when I started building and using customGPTs. #ai #chatgpt #chatgptforcreators #aitools #aibusinessidea #ailearning #aieducation #marketing #business
View originalPricing found: $20.
Meta's new structured prompting technique makes LLMs significantly better at code review — boosting accuracy to 93% in some cases
Deploying AI agents for repository-scale tasks like bug detection, patch verification, and code review requires overcoming significant technical hurdles. One major bottleneck: the need to set up dynamic execution sandboxes for every repository, which are expensive and computationally heavy. Using large language model (LLM) reasoning instead of executing the code is rising in popularity to bypass this overhead, yet it frequently leads to unsupported guesses and hallucinations. To improve execution-free reasoning, researchers at Meta introduce "semi-formal reasoning," a structured prompting technique. This method requires the AI agent to fill out a logical certificate by explicitly stating premises, tracing concrete execution paths, and deriving formal conclusions before providing an answer. The structured format forces the agent to systematically gather evidence and follow function calls before drawing conclusions. This increases the accuracy of LLMs in coding tasks and significantly reduces errors in fault localization and codebase question-answering. For developers using LLMs in code review tasks, semi-formal reasoning enables highly reliable, execution-free semantic code analysis while drastically reducing the infrastructure costs of AI coding systems. Agentic code reasoning Agentic code reasoning is an AI agent's ability to navigate files, trace dependencies, and iteratively gather context to perform deep semantic analysis on a codebase without running the code. In enterprise AI applications, this capability is essential for scaling automated bug detection, comprehensive code reviews, and patch verification across complex repositories where relevant context spans multiple files. The industry currently tackles execution-free code verification through two primary approaches. The first involves unstructured LLM evaluators that try to verify code either directly or by training specialized LLMs as reward models to approximate test outcomes. The major drawback is their
View originalImagine if your Teams or Slack messages automatically turned into secure context for your AI agents — PromptQL built it
For the modern enterprise, the digital workspace risks descending into "coordination theater," in which teams spend more time discussing work than executing it. While traditional tools like Slack or Teams excel at rapid communication, they have structurally failed to serve as a reliable foundation for AI agents, such that a Hacker News thread went viral in February 2026 calling upon OpenAI to build its own version of Slack to help empower AI agents, amassing 327 comments. That's because agents often lack the real-time context and secure data access required to be truly useful, often resulting in "hallucinations" or repetitive re-explaining of codebase conventions. PromptQL, a spin-off from the GraphQL unicorn Hasura, is addressing this by pivoting from an AI data tool into a comprehensive, AI-native workspace designed to turn casual, regular team interactions into a persistent, secure memory for agentic workflows — ensuring these conversations are not simply left by the wayside or that users and agents have to try and find them again later, but rather, distilled and stored as actionable, proprietary data in an organized format — an internal wiki — that the company can rely on going forward, forever, approved and edited manually as needed. Imagine two colleagues messaging about a bug that needs to be fixed — instead of manually assigning it to an engineer or agent, your messaging platform automatically tags it, assigns it and documents it all in the wiki with one click Now do this for every issue or topic of discussion that takes place in your enterprise, and you'll have an idea of what PromptQL is attempting. The idea is a simple but powerful one: turning the conversation that necessarily precedes work into an actual assignment that is automatically started by your own messaging system. “We don’t have conversations about work anymore," CEO Tanmai Gopal said in a recent video call interview with VentureBeat. "You actually have conversations that do the work.” Origina
View originalSoftr launches AI-native platform to help nontechnical teams build business apps without code
Softr, the Berlin-based no-code platform used by more than one million builders and 7,000 organizations including Netflix, Google, and Stripe, today launched what it calls an AI-native platform — a bet that the explosive growth of AI-powered app creation tools has produced a market full of impressive demos but very little production-ready business software. The company's new AI Co-Builder lets non-technical users describe in plain language the software they need, and the platform generates a fully integrated system — database, user interface, permissions, and business logic included — connected and ready for real-world deployment immediately. The move marks a fundamental evolution for a company that spent five years building a no-code business before layering AI on top of what it describes as a proven infrastructure of constrained, pre-built building blocks. "Most AI app-builders stop at the shiny demo stage," Softr Co-Founder and CEO Mariam Hakobyan told VentureBeat in an exclusive interview ahead of the launch. "A lot of the time, people generate calculators, landing pages, and websites — and there are a huge number of use cases for those. But there is no actual business application builder, which has completely different needs." The announcement arrives at a moment when the AI app-building market finds itself at an inflection point. A wave of so-called "vibe coding" platforms — tools like Lovable, Bolt, and Replit that generate application code from natural language prompts — have captured developer mindshare and venture capital over the past 18 months. But Hakobyan argues those tools fundamentally misserve the audience Softr is chasing: the estimated billions of non-technical business users inside companies who need custom operational software but lack the skills to maintain AI-generated code when it inevitably breaks. Why AI-generated app prototypes keep failing when real business data is involved The core tension Softr is trying to resolve is one that has plag
View originalEngenharia de Prompt: Por Que a Forma Como Você Pergunta Muda Tudo(Um guia introdutório)
Neste artigo irei explicar alguns pontos importantes sobre Engenharia de prompt, e como saber esses...
View originalCongrats to the Winners of Our First DEV Weekend Challenge!
It's time!! We are thrilled to announce the winners of our first DEV Weekend Challenge. The prompt...
View originalVibe-coding in Google AI Studio: my tips to prompt better and create amazing apps
You might already know Google AI Studio as a sandbox to play with the Deepmind models and tinker with...
View originalOperating in Prompt Space: Red Teaming the Control Plane of an LLM
Before this post existed, it was a prompt. Before that, a response to a prompt. Before that, a...
View originalShow HN: Tmux-IDE, OSS agent-first terminal IDE
Hey HN,<p>Small OSS project that i created for myself and want to share with the community. It's a declarative, scriptable, terminal-based IDE focussed on agentic engineering.<p>That's a lot of jargon, but essentially its a multi-agent IDE that you start in your terminal.<p>Why is that relevant? Thanks to tmux and SSH, it means that you have a really simple and efficient way to create your own always-on coding setup.<p>Boot into your IDE through ssh, give a prompt to claude and close off your machine. In tmux-ide claude will keep working.<p>The tool is intentionally really lightweight, because I think the power should come from the harnesses that you are working with.<p>I'm hoping to share this with the community and get feedback and suggestions to shape this project! I think that "remote work" is directionally correct, because we can now have extremely long-running coding tasks. But I also think we should be able to control and orchstrate that experience according to what we need.<p>The project is 100% open-source, and i hope to shape it together with others who like to work in this way too!<p>Github: <a href="https://github.com/wavyrai/tmux-ide" rel="nofollow">https://github.com/wavyrai/tmux-ide</a> Docs: <a href="https://tmux.thijsverreck.com/docs" rel="nofollow">https://tmux.thijsverreck.com/docs</a>
View originalThe Attribution Story
The year is 1999. Ask kids who invented the personal computer? "Bill Gates" - a symptom of how...
View originalAnthropic makes a pricing change that matters for Claude’s longest prompts
Anthropic announced Friday that the 1-million-token context window for Claude Opus 4.6 and Claude Sonnet 4.6 is now generally available, The post Anthropic makes a pricing change that matters for Claude’s longest prompts appeared first on The New Stack.
View originalRethinking AEO when software agents navigate the web on behalf of users
For more than two decades, digital businesses have relied on a simple assumption: When someone interacts with a website, that activity reflects a human making a conscious choice. Clicks are treated as signals of interest. Time on page is assumed to indicate engagement. Movement through a funnel is interpreted as intent. Entire growth strategies, marketing budgets, and product decisions have been built on this premise. Today, that assumption is quietly beginning to erode. As AI-powered tools increasingly interact with the web on behalf of users, many of the signals organizations depend on are becoming harder to interpret. The data itself is still accurate — pages are viewed, buttons are clicked, actions are recorded — but the meaning behind those actions is changing. This shift isn’t theoretical or limited to edge cases. It’s already influencing how leaders read dashboards, forecast demand, and evaluate performance. The challenge ahead isn’t stopping AI-driven interactions. It’s learning how to interpret digital behavior in a world where human and automated activity increasingly overlap. A changing assumption about web traffic For decades, the foundation of the internet rested on a quiet, human-centric model. Behind every scroll, form submission, or purchase flow was a person acting out of curiosity, need, or intent. Analytics platforms evolved to capture these behaviors. Security systems focused on separating “legitimate users” from clearly scripted automation. Even digital advertising economics assumed that engagement equaled human attention. Over the last few years, that model has begun to shift. Advances in large language models (LLMs), browser automation, and AI-driven agents have made it possible for software systems to navigate the web in ways that feel fluid and context-aware. Pages are explored, options are compared, workflows are completed — often without obvious signs of automation. This doesn’t mean the web is becoming less human. Instead, it’s becoming m
View originalShow HN: Oxyde – Pydantic-native async ORM with a Rust core
Hi HN! I built Oxyde because I was tired of duplicating my models.<p>If you use FastAPI, you know the drill. You define Pydantic models for your API, then define separate ORM models for your database, then write converters between them. SQLModel tries to fix this but it's still SQLAlchemy underneath. Tortoise gives you a nice Django-style API but its own model system. Django ORM is great but welded to the framework.<p>I wanted something simple: your Pydantic model IS your database model. One class, full validation on input and output, native type hints, zero duplication. The query API is Django-style (.objects.filter(), .exclude(), Q/F expressions) because I think it's one of the best designs out there.<p><i>Explicit over implicit.</i> I tried to remove all the magic. Queries don't touch the database until you call a terminal method like .all(), .get(), or .first(). If you don't explicitly call .join() or .prefetch(), related data won't be loaded. No lazy loading, no surprise N+1 queries behind your back. You see exactly what hits the database by reading the code.<p><i>Type safety</i> was a big motivation. Python's weak spot is runtime surprises, so Oxyde tackles this on three levels: (1) when you run makemigrations, it also generates .pyi stub files with fully typed queries, so your IDE knows that filter(age__gte=...) takes an int, that create() accepts exactly the fields your model has, and that .all() returns list[User] not list[Any]; (2) Pydantic validates data going into the database; (3) Pydantic validates data coming back out via model_validate(). You get autocompletion, red squiggles on typos, and runtime guarantees, all from the same model definition.<p><i>Why Rust?</i> Not for speed as a goal. I don't do "language X is better" debates. Each one is good at what it was made for. Python is hard to beat for expressing business logic. But infrastructure stuff like SQL generation, connection pooling, and row serialization is where a systems language makes sense. So I split it: Python handles your models and business logic, Rust handles the database plumbing. Queries are built as an IR in Python, serialized via MessagePack, sent to Rust which generates dialect-specific SQL, executes it, and streams results back. Speed is a side effect of this split, not the goal. But since you're not paying a performance tax for the convenience, here are the benchmarks if curious: <a href="https://oxyde.fatalyst.dev/latest/advanced/benchmarks/" rel="nofollow">https://oxyde.fatalyst.dev/latest/advanced/benchmarks/</a><p>What's there today: Django-style migrations (makemigrations / migrate), transactions with savepoints, joins and prefetch, PostgreSQL + SQLite + MySQL, FastAPI integration, and an auto-generated admin panel that works with FastAPI, Litestar, Sanic, Quart, and Falcon (<a href="https://github.com/mr-fatalyst/oxyde-admin" rel="nofollow">https://github.com/mr-fatalyst/oxyde-admin</a>).<p>It's v0.5, beta, active development, API might still change. This is my attempt to build the ORM I personally wanted to use. Would love feedback, criticism, ideas.<p>Docs: <a href="https://oxyde.fatalyst.dev/" rel="nofollow">https://oxyde.fatalyst.dev/</a><p>Step-by-step FastAPI tutorial (blog API from scratch): <a href="https://github.com/mr-fatalyst/fastapi-oxyde-example" rel="nofollow">https://github.com/mr-fatalyst/fastapi-oxyde-example</a>
View original[Resource]: WRITE THE NAME OF YOUR RESOURCE HERE
### Display Name AI.MD ### Category Agent Skills ### Sub-Category General ### Primary Link https://github.com/sstklen/ai-md ### Author Name sstklen ### Author Link https://github.com/sstklen ### License MIT ### Other License _No response_ ### Description Converts human-written CLAUDE.md files into AI-native structured-label format using a 6-phase methodology (understand, decompose, label,structure, resolve, test). Battle-tested with 4 LLM models — structured format raised Codex (GPT-5.3) compliance from 6/8 to 8/8 on identical rule content, while reducing file size by 53% and line count by 37% (224 → 142 lines, within Claude Code's recommended 200-line limit). ### Validate Claims Install the skill and run it on any CLAUDE.md over 100 lines. The skill measures before/after byte count and line count, converts to structured-label format with automatic backup (.bak), and reports the diff. Real test data is in the README (4-model comparison table). The examples/ directory contains a complete before/after pair for manual inspection. ### Specific Task(s) Have Claude Code convert an existing CLAUDE.md using the AI.MD skill. Then compare compliance by testing both versions (original backup vs converted) with the same set of questions. The skill's SKILL.md documents the exact 8-question test protocol used in validation. ### Specific Prompt(s) Say: "distill my CLAUDE.md" or "AI.MD" — the skill previews current token cost, shows before/after examples, then offers to convert with full backup. After conversion, say "test my CLAUDE.md" to run the built-in multi-model validation. ### Additional Comments The core insight: LLMs re-read CLAUDE.md every conversation turn. Human prose wastes tokens and splits attention across rules sharing a line. Structured-label format (one concept per line, explicit trigger/action/exception labels, XML section boundaries) gives each rule full attention weight. This is not compression — it's restructuring the same rules into a format LLMs parse more reliably. Full methodology: 6 conversion phases + 5 special techniques documented in SKILL.md (525 lines). ### Recommendation Checklist - [x] I have checked that this resource hasn't already been submitted - [x] It has been over one week since the first public commit to the repo I am recommending - [x] All provided links are working and publicly accessible - [x] I do NOT have any other open issues in this repository - [x] I am primarily composed of human-y stuff and not electrical circuits
View original[BUG] /usage command fails with rate_limit_error when checking usage data
**What's Wrong?** When running the `/usage` command to check current usage and limits in Claude Code, the UI displays an error: ``` Error: Failed to load usage data: {"error":{"message":"Rate limited. Please try again later.","type":"rate_limit_error"}} ``` The `/usage` dialog opens but immediately fails to load any data, making it impossible to monitor current token usage or remaining quota. **What Should Happen?** The `/usage` dialog should successfully load and display current session usage statistics, token consumption, and remaining quota without being rate limited by the usage data endpoint itself. **Steps to Reproduce** 1. Open Claude Code in an active session 2. Type `/usage` and press Enter 3. Observe the error: "Error: Failed to load usage data: rate_limit_error" **Additional Context** This appears related to #31637 where the `/api/oauth/usage` endpoint aggressively rate limits usage monitoring requests. The error occurs on the Anthropic Max plan. **Environment** - Claude Code version: 2.1.71 (Claude Code) - OS: Windows 10 Pro (Build 19045) - Terminal: Windows Terminal 1.23.20211.0 - Platform: Anthropic Max
View originalchore(deps): update updates-patch-minor
> ℹ️ **Note** > > This PR body was truncated due to platform limits. This PR contains the following updates: | Package | Update | Change | |---|---|---| | 1password/connect-sync | patch | `1.8.1` → `1.8.2` | | [alpine/openclaw](https://openclaw.ai) ([source](https://redirect.github.com/openclaw/openclaw)) | minor | `2026.2.22` → `2026.3.8` | | [cloudflare/cloudflared](https://redirect.github.com/cloudflare/cloudflared) | minor | `2026.2.0` → `2026.3.0` | | kerberos/agent | patch | `v3.6.12` → `v3.6.15` | | [searxng/searxng](https://searxng.org) ([source](https://redirect.github.com/searxng/searxng)) | patch | `2026.3.8-a563127a2` → `2026.3.9-d4954a064` | --- > [!WARNING] > Some dependencies could not be looked up. Check the [Dependency Dashboard](../issues/304) for more information. --- ### Release Notes <details> <summary>openclaw/openclaw (alpine/openclaw)</summary> ### [`v2026.3.8`](https://redirect.github.com/openclaw/openclaw/blob/HEAD/CHANGELOG.md#202638) [Compare Source](https://redirect.github.com/openclaw/openclaw/compare/v2026.3.7...v2026.3.8) ##### Changes - CLI/backup: add `openclaw backup create` and `openclaw backup verify` for local state archives, including `--only-config`, `--no-include-workspace`, manifest/payload validation, and backup guidance in destructive flows. ([#​40163](https://redirect.github.com/openclaw/openclaw/issues/40163)) thanks [@​shichangs](https://redirect.github.com/shichangs). - macOS/onboarding: add a remote gateway token field for remote mode, preserve existing non-plaintext `gateway.remote.token` config values until explicitly replaced, and warn when the loaded token shape cannot be used directly from the macOS app. ([#​40187](https://redirect.github.com/openclaw/openclaw/issues/40187), supersedes [#​34614](https://redirect.github.com/openclaw/openclaw/issues/34614)) Thanks [@​cgdusek](https://redirect.github.com/cgdusek). - Talk mode: add top-level `talk.silenceTimeoutMs` config so Talk waits a configurable amount of silence before auto-sending the current transcript, while keeping each platform's existing default pause window when unset. ([#​39607](https://redirect.github.com/openclaw/openclaw/issues/39607)) Thanks [@​danodoesdesign](https://redirect.github.com/danodoesdesign). Fixes [#​17147](https://redirect.github.com/openclaw/openclaw/issues/17147). - TUI: infer the active agent from the current workspace when launched inside a configured agent workspace, while preserving explicit `agent:` session targets. ([#​39591](https://redirect.github.com/openclaw/openclaw/issues/39591)) thanks [@​arceus77-7](https://redirect.github.com/arceus77-7). - Tools/Brave web search: add opt-in `tools.web.search.brave.mode: "llm-context"` so `web_search` can call Brave's LLM Context endpoint and return extracted grounding snippets with source metadata, plus config/docs/test coverage. ([#​33383](https://redirect.github.com/openclaw/openclaw/issues/33383)) Thanks [@​thirumaleshp](https://redirect.github.com/thirumaleshp). - CLI/install: include the short git commit hash in `openclaw --version` output when metadata is available, and keep installer version checks compatible with the decorated format. ([#​39712](https://redirect.github.com/openclaw/openclaw/issues/39712)) thanks [@​sourman](https://redirect.github.com/sourman). - CLI/backup: improve archive naming for date sorting, add config-only backup mode, and harden backup planning, publication, and verification edge cases. ([#​40163](https://redirect.github.com/openclaw/openclaw/issues/40163)) Thanks [@​gumadeiras](https://redirect.github.com/gumadeiras). - ACP/Provenance: add optional ACP ingress provenance metadata and visible receipt injection (`openclaw acp --provenance off|meta|meta+receipt`) so OpenClaw agents can retain and report ACP-origin context with session trace IDs. ([#​40473](https://redirect.github.com/openclaw/openclaw/issues/40473)) thanks [@​mbelinky](https://redirect.github.com/mbelinky). - Tools/web search: alphabetize provider ordering across runtime selection, onboarding/configure pickers, and config metadata, so provider lists stay neutral and multi-key auto-detect now prefers Grok before Kimi. ([#​40259](https://redirect.github.com/openclaw/openclaw/issues/40259)) thanks [@​kesku](https://redirect.github.com/kesku). - Docs/Web search: restore $5/month free-credit details, replace defunct "Data for Search"/"Data for AI" plan names with current "Search" plan, and note legacy subscription validity in Brave setup docs. Follows up on [#​26860](https://redirect.github.com/openclaw/openclaw/issues/26860). ([#​40111](https://redirect.github.com/openclaw/openclaw/issues/40111)) Thanks [@​remusao](https://redirect.github.com/remusao). - Extensions/ACPX tests: move the shared runtime fixture helper from `src/runtime-internals/` to `src/test-utils/` so the test-only he
View originalPricing found: $20.
Based on user reviews and social mentions, the most common pain points are: token cost, token usage, API costs, claude.
Based on 75 social mentions analyzed, 0% of sentiment is positive, 100% neutral, and 0% negative.
Dharmesh Shah
CTO at HubSpot
3 mentions
