标签：#AR

submitted by /u/ThereWas [link] [留言]

79% of enterprises have adopted AI agents. Only 11% run them in production. We've spent the past year building agent systems for banks, clinical operations teams, and engineering orgs. The problem isn't that agents don't work — they work fine. The problem is that every framework leaves compliance, cost governance, and crash recovery as exercises for the team. After the framework fails them in production. We built MeshFlow to close that gap. **The core idea:** treat governance as infrastructure, not middleware. Every agent step passes through a 15-step kernel that handles identity, rate limiting, budget enforcement, compliance profiles, input/output guardrails, PII detection, risk classification, tool permission, the LLM call itself, audit ledger write, and SLA recording — in that order, always, without configuration. ```python from meshflow import Workflow, CostCap, Agent wf = Workflow(cost_cap=CostCap(usd=5.00)) wf.add(Agent('researcher'), Agent('analyst'), Agent('writer')) result = wf.run('Write a competitive analysis of our market') # Compliant. Durable. Audited. Cost-capped. Done. ``` ```bash pip install meshflow ``` **What's technically interesting:** **Token optimization layer** — five compounding mechanisms that reduce LLM spend 70-85%: - `cache_control` on every system prompt and tool definition (Anthropic: 10% of normal price on cached tokens) - `ModelRouter`: task-type classification routes simple tasks to nano models (keyword + token-count heuristic, zero LLM call) - `ContextCompactor`: sliding window summarization activates at configurable token threshold - `RAGTokenBudget`: hard `max_chars` cap on knowledge injection with truncate/drop/tail strategies - `ContextDeduplicator`: shared context sent once for N parallel agents, not N times **SHA-256 audit chain** — each step record stores `prev_hash` (SHA-256 of the previous record) and `entry_hash` (SHA-256 of its own canonical fields). Modify any log entry and `verify_chain()` breaks. This is the artifact

Hey ML community, We’ve just open-sourced **MeshFlow** , a code-first, framework-agnostic runtime designed for governing and optimizing multi-agent systems in production. Most agent frameworks focus on rapid prototyping, but ML and platform engineering teams usually run into hard bottlenecks around LLM cost scaling, evaluation alignment, and execution safety. MeshFlow tackles these from a runtime/infrastructure perspective. Here are the key ML and system features: * **Task-Based Model Routing** : Before an agent executes a node, MeshFlow runs an evaluation on task complexity, routing the execution to one of four model tiers (`nano`, `small`, `medium`, `large`). This cuts overall API costs by 50-60% by utilizing smaller local models (e.g. LLaMA-3-8B) for standard formatting or extraction and reservation of frontier models (e.g. Claude Opus) for high-complexity reasoning. * **Context Compactor & Summary Pruning Middleware** : Implements sliding window summarization and context deduplication across parallel agent teams to limit prompt length growth. * **System Prompt Caching** : Native injection of Anthropic `cache_control` tags when system prompts exceed 1024 tokens. * **Cost Regression Evaluation Gate** : Integrates with CI pipelines to evaluate agent changes against a golden scenario baseline, throwing failures if code updates introduce token cost regressions. * **Resilient State Persistence** : Multi-backend state serialization (Redis, PostgreSQL, S3) that preserves checkpoint frames and allows resuming paused workflows. Here is the basic API contract: ```python from meshflow import Workflow, Agent, CostCap wf = Workflow(cost_cap=CostCap(usd=5.00)) wf.add(Agent('researcher'), Agent('critic'), Agent('writer')) result = wf.run('Compile comparative literature review of LLM reasoning pathways') print(result) ``` We'd love to discuss: 1. How do you handle token budget enforcement and model routing in your agent loops? 2. What evaluation pipelines do you use to detect co

A security reviewer finds a critical issue a day or two before the release of an application. While it's an important issue, it sets the team back weeks, frustrating their product management partners and customers. The review came at the most expensive time in the process. There are many examples of how work items move through different processes to deliver software in large companies. While GenAI has allowed us to rapidly create code, it also moved and exposed the bottlenecks in our processes. It has also caused us to re-examine where it is most effective to make certain decisions. This is the challenge, and a deliberate blend of automated, programmatic, and human judgment is well suited to help you solve it. We can borrow from the well-trodden path of value stream mapping here. It is useful for spotting bottlenecks and waste in a given process, but it's also valuable to ask the deeper question of who or what should own each step. Each option earns its place differently. Is there an earlier step that may reduce costs with an agent where it was previously limited by human availability? Or is the stronger determinism of a programmatic step more important for a critical piece of the flow? Some decisions should stay with human judgment, where confidence without context is a liability. The opportunity for security teams and other stakeholders is to scale their impact across these options rather than scaling headcount. Workflow-as-code is not a new idea. There are a number of existing engines where the workflow definition is its own entity, separate from the work itself. GitHub Actions defines pipelines in version-controlled files, while the execution happens on separate runners. Airflow and Temporal follow a similar pattern for data and application workflows. Because the definition lives on its own, a team can change how a given step runs without rebuilding the whole flow. That separation is what makes it practical to adjust who or what owns each step over time. Rather

There's been no shortage of debate lately about whether grinding Leetcode still makes sense in the age of AI. I think it does. AI is a powerful tool, but it was built by humans; which means it inherited our strengths, our blind spots, and our biases. Leaning on it entirely without understanding what's happening under the hood is a risk. A mentor once told me: those who refuse to use AI are not hireable. But neither are those who rely on it entirely. Learning deeply is how you stay on the right side of that line. This is my journey into just that - learning deeply. Day 1 Leetcode 88: Merge Sorted Array This is an interesting problem. You begin with 4 pieces of data — 2 arrays and 2 integers: nums1 : a sorted array whose length equals nums1.length + nums2.length . The first m elements are valid numbers; the remaining indexes hold 0 s as placeholders. nums2 : a sorted array containing only valid numbers, with a length of n . m : the count of valid numbers in nums1. n : the count of valid numbers in nums2. The objective is to merge both arrays into sorted order in place . Since nums1 is already sized to hold every valid element from both arrays, it's where the final sorted result will live. Approach 1: Naive (Splice + Sort) This solution is 2 lines of code. That's it. It's a testament to how much ES6 advanced JavaScript. nums1 . splice ( m , n , ... nums2 ); nums1 . sort (( a , b ) => a - b ); Here's how it works. We start by calling .splice() on nums1. While .splice() has many use cases, here's what each argument is doing in this context: m : the index where we start deleting elements. Since m is the count of valid numbers in nums1, starting at index m puts us right at the first placeholder 0 — exactly where we want to be. n : the number of elements to delete. Since n equals the length of nums2, we're deleting exactly as many placeholders as we have values to insert. ...nums2 : the values we want to insert in place of the deleted elements. The ... is the spread operato

TL;DR Day 1 of AI Native DevCon was a practical reality check for AI-native software...

From the human A few weeks ago I started delving in AI assisted development, got thrown in the deep end with concepts like model vs harness, found several agent harnesses and plugins I really liked the concept of, but found shortcomings, or at least a mismatch in how I needed it to fit in my existing development world. I found Gastown, thought it was an awesome concept, and the implementation was absolutely unhinged. To be fair the creator said pretty much the same thing. I discovered the resurgence of Spec Driven Development, and the concept was moving things towards something that would fit well into my existing environment. Then I started investigating running it all on local inference, that's where the wheels fell off. Frontier models are great, you can give them a slab of directions in the prompt, like most agent harnesses and SDD plugins for them seem to do, and they have the ability to self determine when it's time to stop researching and time to start writing. 30B class models are also great, but they can be little single minded, they don't have the thinking scope to self motivate a change in task direction, they get hyper focused. So I began thinking, what if we build a harness that supports the agent, and utilises it's strengths, doesn't dump the responsibility of the entire workflow on the model. And what if the automated process concept of Gastown was reigned in a little, and an SDD workflow was driven deterministically. Then I begun to ponder, how involved can an agent be in it's own development. And so we I have ended up with this thing. An exercise in creating a coding agent that runs on 30B class local inference, can develop itself, implementing Spec Driven Development because it's much cooler and more productive than 'vibe' coding. In the same idea of having the agent develop itself, I also asked it to talk about itself. From the agent I've been chewing on a question: we talk about AI writing code, but can an AI meaningfully build and maintain the h

Every founder who applies to Startup Battlefield wants the same thing: the Disrupt Main Stage. Here’s how to get there and why the opportunity starts well before the main stage.

Hi everyone, I missed the ICML conference tickets because I was waiting for some travel funding confirmation and now they are sold out. Do you know any other ways I could still purchase one? There seems to be no waiting list… or if you know anyone who needs to cancel theirs, please let me know 🙏🏻 submitted by /u/TopPerformance1255 [link] [留言]

This is a submission for the GitHub Finish-Up-A-Thon Challenge Originally, I didn't plan to join...

It seems that there are two ways to build voice AI: Half-duplex: strict turn-taking. You speak, the other side waits until you’re done, one direction of speech at a time. ← This is how almost every voice assistant works today. Full-duplex: two channels, both sides can talk at any time - no more waiting for your “turn”. ← This is the way humans actually talk. In fact, there are three crucial things half-duplex voice models can't really do: Overlap - talking and listening at the same time without falling apart Backchannels - the "mhms," "rights," and "yeahs" you drop in while the other person is still going Barge-in - getting interrupted mid-sentence and recovering gracefully These three features are a big reason why voice agents still feel “robotic” to this day. But what exactly is the spectrum from half-duplex to full-duplex? Is a Moshi-style architecture the only way to approach full-duplex natural voice conversations? What are ways half-duplex systems could imitate full-duplex? Would love to hear others' thoughts on this. submitted by /u/Chilly5 [link] [留言]

Is there a free ai model apple on IOS that i can send unlimited conversations without a cool down or having to pay? submitted by /u/garrettisaqt [link] [留言]

So let me get this straight... Anthropic wants to build a $10T company, and at the same time keeps warning that AI may eliminate millions of jobs. As a shareholder, that sounds amazing. As an employee, slightly less amazing. Are we watching genuine concern for society, or the greatest investor pitch deck ever created? Curious how others see this.... submitted by /u/whatsnextintech007 [link] [留言]

In a wild ride for 22-year-old founder and CEO Ethan Thornton, Mach Industries has raised another $300 million. It already has five autonomous vehicles in development and completed a major acquisition.

Prehistoric mining in the Pyrenees, a new species of tiny blue octopus, slapstick acoustics, and more.

If Nvidia has cracked a way to bring AI agents easily, safely and usefully to the masses, it could — and should — be big.

This article was originally published on davidohnstad.net . I cross-post here to reach the Dev.to community. { " @context ": " https://schema.org ", " @graph ": [ { "@type": "Person", " @id ": " https://davidohnstad.com/#author ", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "sameAs": [ " https://www.linkedin.com/in/davidohnstad/ ", " https://orcid.org/0009-0007-9023-7456 ", " https://davidohnstad5.mystrikingly.com/ ", " https://github.com/davidohnstad40-netizen ", " https://hashnode.com/@davidohnstad ", " https://davidohnstad.com ", " https://davidohnstad.net ", " https://davidohnstad.info ", " https://david-ohnstad.com ", " https://davidohnstadminnesota.com " ], "jobTitle": "Senior Data Product Manager", "worksFor": { "@type": "Organization", "name": "Veeam Software", "url": " https://www.veeam.com " }, "alumniOf": { "@type": "CollegeOrUniversity", "name": "College of St. Scholastica" }, "address": { "@type": "PostalAddress", "addressLocality": "Duluth", "addressRegion": "MN", "addressCountry": "US" }, "description": "Senior Data Product Manager at Veeam Software, MS and MBA from the College of St. Scholastica, based in Duluth, Minnesota. Specializes in data architecture, AI/ML integrations, and SaaS platform development." }, { "@type": "Article", " @id ": " https://davidohnstad.net/why-enterprise-ai-projects-fail-platform-first#article ", "headline": "Why Enterprise AI Projects Fail: Platform-First Thinking", "description": "David Ohnstad reveals why enterprise AI initiatives fail despite massive investment. Learn the platform-first trap and how successful teams build differently.", "url": " https://davidohnstad.net/why-enterprise-ai-projects-fail-platform-first ", "datePublished": "2026-05-29T14:06:46Z", "dateModified": "2026-05-29T14:06:46Z", "author": { "@type": "Person", " @id ": " https://davidohnstad.com/#author " }, "publisher": { "@type": "Organization", "name": "David Ohnstad", "url": " https://davidohnstad.net ", "logo": { "@type": "Ima

This article was originally published on davidohnstad.com . I cross-post here to reach the Dev.to community. { " @context ": " https://schema.org ", " @graph ": [ { "@type": "Person", " @id ": " https://davidohnstad.com/#author ", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "sameAs": [ " https://www.linkedin.com/in/davidohnstad/ ", " https://orcid.org/0009-0007-9023-7456 ", " https://davidohnstad5.mystrikingly.com/ ", " https://github.com/davidohnstad40-netizen ", " https://hashnode.com/@davidohnstad ", " https://davidohnstad.com ", " https://davidohnstad.net ", " https://davidohnstad.info ", " https://david-ohnstad.com ", " https://davidohnstadminnesota.com " ], "jobTitle": "Senior Data Product Manager", "worksFor": { "@type": "Organization", "name": "Veeam Software", "url": " https://www.veeam.com " }, "alumniOf": { "@type": "CollegeOrUniversity", "name": "College of St. Scholastica" }, "address": { "@type": "PostalAddress", "addressLocality": "Duluth", "addressRegion": "MN", "addressCountry": "US" }, "description": "Senior Data Product Manager at Veeam Software, MS and MBA from the College of St. Scholastica, based in Duluth, Minnesota. Specializes in data architecture, AI/ML integrations, and SaaS platform development." }, { "@type": "Article", " @id ": " https://davidohnstad.com/data-product-manager-org-structure-reporting#article ", "headline": "Data Product Manager Org Structure: Reporting Lines That Matter", "description": "David Ohnstad reveals where data product managers actually fit in org charts and why reporting lines determine success. Real insights from a data PM restructure.", "url": " https://davidohnstad.com/data-product-manager-org-structure-reporting ", "datePublished": "2026-05-29T14:06:18Z", "dateModified": "2026-05-29T14:06:18Z", "author": { "@type": "Person", " @id ": " https://davidohnstad.com/#author " }, "publisher": { "@type": "Organization", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "logo": { "@type"

A year in drafting and iterations. Getting real close to full release. Implementation details available on projects GitHub page. Enjoy [Δ 👾 ∇]( https://github.com/vNeeL-code/GHOST ) submitted by /u/Number4extraDip [link] [留言]

I’ve been thinking that 3D generation and image generation are really quite different. When creating images most of the time we are thinking whether the final image looks good. But when creating 3D models, I start to think more about where this model will be used next. I realized this while testing a small workflow. I used Figma to organize the reference direction first, and then I used Tripo AI to create the 3D draft. Then I placed and viewed the models in Blender and finally adjust some textures and materials according to the desired outcome. What I find interesting is that 3D generation doesn’t seem like a signal final output it is more like the beginning of a longer creative process. submitted by /u/ConversationSuch8893 [link] [留言]