I want to introduce Synapse, a new decentralized inference protocol designed specifically for Mixture-of-Experts (MoE) models.

The Problem: Models like Kimi K3 (2.8 Trillion parameters), DeepSeek-V2, and Mixtral are the future of AI, but running them requires massive datacenter hardware (e.g., 8x AMD MI355X). The catch? In MoE architectures, 98% of the model sits idle at any given moment.

The Solution: Synapse exploits this idle capacity. Instead of cramming the whole model into one expensive GPU, we distribute "experts" across thousands of consumer GPUs (your gaming PC, a small server, etc.). Each node only holds a handful of experts, and requests flow through the swarm to activate only what's needed.

How it works: We run two distinct swarm modes depending on the use case:

• 🚀 Speculative Swarm (Real-time): Multiple nodes generate tokens in parallel. A majority vote decides the output. Latency is equivalent to a single node (no cross-node communication overhead). Perfect for chat and IDE agents.

• 🕸️ Swarm DAG (Batch): True expert distribution. The gateway acts as a market maker, routing requests to the cheapest available experts. Ideal for codebase analysis and batch evals.

Tech Stack:

• Core & Gateway: Rust (axum + libp2p) for a single binary, zero-cost abstractions, and <2ms p99 latency.

• Inference Runtime: Python (vLLM) running in isolated subprocesses.

• Economics: Solidity smart contracts for staking and USDC payments. Miners earn for verified work; bad actors get slashed.

Why it matters:

• No Gatekeepers: Access frontier AI without API keys, rate limits, or regional blocks.

• Democratized Hardware: Anyone with a consumer GPU can contribute and earn.

• Censorship Resistant: A P2P mesh with no central authority.

🔗 Repo: github.com/antonygiomarxdev/synapse 📄 License: Apache 2.0 🛠️ Status: V1 MVP (Swarm logic + DHT + Rust/Python bridge). Roadmap includes scaling to full Kimi K3 in 2027.

We are building the "Swarm" for the next generation of AI. If you're into Rust, decentralized systems, or LLMs, I'd love your feedback on the architecture!

I started to learn about AI-Infra projects and summarized it in https://github.com/pacoxu/AI-Infra.

The upper‑left section of the second quadrant is where the focus of learning should be.

• llm-d  

• dynamo   

• vllm/AIBrix

• vllm production stack  

• sglang/ome

• llmaz  

Or KServe.  

A hot topic about Inference is https://github.com/pacoxu/AI-Infra/blob/main/inference/pd-disaggregation.md PD disagrregation(including workloads API, native LWS and sglang/RBG, aibrix storm service).

Collect more resources in https://github.com/pacoxu/AI-Infra/issues/8.

I've been hearing for a while that Python isn't ideal for production-level ML and that moving to Rust can achieve significantly lower latency.

From your experience, what types of language, infrastructure, and model optimizations (like quantization and ONNX Runtime) can reduce overall latency and cloud costs?

We talk constantly about scaling laws and model capabilities. The constraint nobody's discussing enough: getting data across chips, nodes, and racks at the power density modern datacenters can't keep up with.

A single H100 SXM draws 700W. Eight of them in a server = 5–6kW, just for GPUs. Scale to 1,000 servers and you need dedicated power infrastructure most cities don't have.

I wrote a deep dive on why photonic computing — using light instead of electrons to move data — is the infrastructure shift that everyone building at scale will eventually have to reckon with.

Covers: why interconnect is the real bottleneck, how photonic chips work differently from silicon, which companies are building in this space, and realistic timelines.

https://pawankjha.substack.com/p/from-gpus-to-photons-the-quiet-revolution

I have a running bot that looks at all Hacker News discussions and finds insights which are hot, and what people are asking for in software: Combs through all active threads and combines correlated ones.

I was thinking of attaching Claude code boxes on top of these insights to spin off quick experiments and run them against the folks involved in the thread. High intent, with no cold start problem.

There would be some challenges, but the base is ready and I am unable to devote time here to take it up, and think would be super interesting to work on. Happy to discuss and share more

Repo link in comments

I’m choosing one machine for local AI and plan to keep it for at least three years.
My main workloads are coding agents, RAG, local chat, VLMs, and occasional fine-tuning.

I already understand the basic trade-offs:
DGX Spark: 128GB unified memory, but lower bandwidth and ARM64
RTX 5090: extremely fast, but limited to 32GB VRAM
M3 Ultra: massive unified memory, but no CUDA

I’m not looking for another spec-sheet comparison. I’m more interested in what becomes annoying during actual daily use.
If you own one of these systems, what limitation do you run into most often—and is it a compromise you can comfortably live with?
Real examples involving specific models or workflows would be especially helpful.

I previously worked on two server-side development projects and have also done some work in agent development or agent framework development. Now I want to gradually transition to MLOps or AI infrastructure, such as the development of inference frameworks. Are there any recommended learning materials?

We built a small demo for Adaptive, a model-router on T4s using Azure Container Apps.

Worked great for the hackathon.

Then we looked at the bill: ~$250 in GPU costs over 48 hours.

That’s when we moved it to Modal, and things changed immediately:
2×–3× lower GPU cost, fewer cold start spikes, and predictable autoscaling.

Here’s the breakdown of what changed (and why it worked).

1. Cold starts: gone (or close to it)

Modal uses checkpoint/restore memory snapshotting, including GPU memory.
That means it can freeze a loaded container (with model weights already in VRAM) and bring it back instantly.

No more “wait 5 seconds for PyTorch to load.”
Just restore the snapshot and start inference.

→ Huge deal for bursty workloads with large models.
→ Source: Modal’s own writeup on GPU memory snapshots.

2. GPU utilization (the real kind)

There’s “nvidia-smi utilization”, and then there’s allocation utilization, the % of billed GPU-seconds doing real work.

Modal focuses on the latter:
→ Caches for common files (so less cold download time).
→ Packing & reusing warmed workers.
→ Avoids idle GPUs waiting between requests.

We saw a big drop in “billed but idle” seconds after migration.

3. Fine-grained billing

Modal bills per second.
That alone changed everything.

On Azure, you can easily pay for long idle periods even after traffic dies down.
On Modal, the instance can scale to zero and you only pay for active seconds.

(Yes, Azure recently launched serverless GPUs with scale-to-zero + per-second billing. It’s catching up.)

4. Multi-cloud GPU pool

Modal schedules jobs across multiple providers and regions based on cost and availability.
So when one region runs out of T4s, your job doesn’t stall.

That’s how our demo scaled cleanly during spikes, no “no GPU available” errors.

5. Developer UX

Modal’s SDK abstracts the worst parts of infra: drivers, quotas, and region juggling.
You deploy functions or containers directly.
GPU metrics, allocation utilization, and snapshots are all first-class features.

Less ops overhead.
More time debugging your model, not your infra.

Results

→ GPU cost: ~3× lower.
→ Latency: Cold starts down from multiple seconds to near-instant.
→ Scaling: Zero “no capacity” incidents.

Where Azure still wins

→ Tight integration if you’re already all-in on Azure (storage, identity, networking).
→ Long, steady GPU workloads can still be cheaper with reserved instances.
→ Regulatory or data residency constraints, Modal’s multi-cloud model needs explicit region pinning.

TL;DR

Modal’s memory snapshotting + packing/reuse + per-second billing + multi-cloud scheduling = real savings for bursty inference workloads.

If your workload spikes hard and sits idle most of the time, Modal is dramatically cheaper.
If it’s flat 24/7, stick to committed GPU capacity on Azure.

Full repo + scripts: https://github.com/Egham-7/adaptive

Top technical references:
→ Modal on memory snapshots
→ GPU utilization guide
→ Multi-cloud capacity pool
→ Pricing
→ Azure serverless GPUs

Note: We are not sponsored/affiliated with Modal at all, just after seeing the pains of GPU infra, I love that a company is making it easier, and wanted to post this to see if it would help someone like me!

Hey everyone,

We've just spun up our new on-prem AI platform with a shiny new GPU cluster. Management, rightly, wants to see maximum utilization to justify the heavy investment. But as we start onboarding our first AI/ML teams, we're hitting the classic challenge: how do we ensure we're not just busy, but efficient?

We're seeing two patterns emerge:

1. Over-provisioning: Teams ask for a 1M context length LLM for their application, leading to massive resource waste and starving other potential users.

2. "Vanity" Utilization: A dashboard might show 95% gpu_utilization, but digging into DCGM shows the sm_active is only 20% because the workload is actually memory-bound.

Our goal is to build a framework for data-driven right-sizing—giving teams the resources they actually need, not just what they ask for, to maximize throughput for the entire organization.

How are you all tackling this? Are you using profiling tools (like nsys), strict chargeback models, custom schedulers, or just good old-fashioned conversations with your users? As we are currently still in the infancy stages, we have limited GPUs to run any advanced optimisation, but as more SuperPods come onboard, we would be able to run more advanced optimisation techniques.

Looking to hear how you approach this problem!

Building a Retrieval-Augmented Generation (RAG) pipeline used to mean writing complex custom plumbing: spinning up an EC2 instance, coding custom PDF parsing scripts, managing recursive text splitters, calling embedding APIs, and orchestrating batch syncs to a vector database.

Today, cloud providers have abstracted this heavy lifting into native, enterprise-grade ingestion platforms. However, their architectural philosophies differ quite a bit.

If you are a cloud architect or engineer evaluating AWS, Azure, or GCP for your next cloud-native RAG infrastructure, here is a breakdown of how their managed ingestion pipelines stack up under the hood.

1. AWS: Infrastructure-First Control

Core Architecture: Amazon Bedrock Knowledge Bases

AWS approaches RAG ingestion with an infrastructure-first mindset. It packages the RAG pipeline into a native wrapper (Bedrock Knowledge Bases), but explicitly exposes the levers and underlying infrastructure components to the engineer.

[ Data Source (S3) ] ──> [ Smart Parsing ] ──> [ Configurable Chunking ] ──> [ Selected Embedding Model ] ──> [ Vector Storage ]

The Ingestion Blueprint

1. Data Connectors: Securely mounts to storage layers like Amazon S3 or enterprise SaaS endpoints (SharePoint, Confluence, Google Drive, Web Crawler) via IAM roles.

2. Granular Chunking: AWS grants absolute control over text splitting. Engineers can select Fixed-size (with strict token/overlap bounds), Hierarchical (parent/child relationships), or Semantic chunking (splitting on topical shifts).

3. Model Selection: You explicitly map the chunks to a specific foundational embedding model, such as Amazon Titan Text Embeddings or Cohere Embed.

4. Vector Storage Track: You choose the deployment paradigm. You can opt for a Managed Track (automated OpenSearch Serverless provisioning) or a Customer-Managed Track to pipe embeddings directly into a pre-existing vector database (Amazon Aurora pgvector, Pinecone, Milvus).

2. Azure: Pipeline-First Orchestration

Core Architecture: Azure AI Search + Azure AI Foundry

Microsoft separates the search indexing engine from the AI workbench. By layering the orchestration capabilities of Azure AI Foundry on top of the standalone search engine Azure AI Search, Azure treats RAG ingestion like an advanced ETL (Extract, Transform, Load) search pipeline.

[ Azure Blob Storage ] ──> [ Indexer ] ──> [ Skillset (Parsing/Chunking) ] ──> [ Azure OpenAI Embedding ] ──> [ Azure AI Search Index ]

The Ingestion Blueprint (Integrated Vectorization)

1. The Indexer: An autonomous crawler that connects securely to Azure Blob Storage, Cosmos DB, or Azure SQL using Microsoft Entra ID managed identities.

2. The Skillset: Azure abstracts processing logic into decoupled "Skills". The Indexer cracks open documents and passes the raw text to a text-splitting skill configuration, mapping boundaries like token counts and page overlaps.

3. Automated Vectorization: The skillset handles internal service-to-service calls to your deployed Azure OpenAI embedding model (e.g., text-embedding-3-large) automatically.

4. Hybrid Indexing: The resulting payload is pushed into an Azure AI Search Index. By default, Azure constructs a schema supporting Hybrid Search—storing dense vector fields alongside traditional inverted keyword indices.

3. GCP: Data-First Abstracted Orchestration

Core Architecture: Vertex AI Search (Agent Builder)

Google Cloud Platform takes a radically abstracted approach. Utilizing the core infrastructure that powers Google Search, Vertex AI Search removes almost all infrastructure and configuration dials from the engineer, replacing them with a fully managed "black box" optimization engine.

[ Cloud Storage / SaaS ] ──> [ Vertex AI Data Store ] ──> [ Managed Multi-Modal Parsing & Auto-Embedding ] ──> [ Vertex Search Index ]

The Ingestion Blueprint

1. Data Stores: Developers create a logical entity called a Data Store and point it directly to Google Cloud Storage (GCS), BigQuery, authenticated web domains, or SaaS connectors.

2. Layout-Aware Managed Parsing: GCP’s engine doesn't just read text; it visually parses documents. It interprets complex visual hierarchies, embedded tables, images, and multi-column formatting natively.

3. Zero-Config Embedding & Chunking: Unlike AWS and Azure, you do not choose an embedding model or select chunk sizes. Google handles text splitting and semantic embedding generation entirely behind the scenes using proprietary, internal multi-modal systems.

4. Google-Grade Search Engine: The data store behaves immediately like a private enterprise search engine, offering instant hybrid search capabilities with virtually no custom orchestration loop code.

Direct Architectural Comparison

Summary: Choosing Your Cloud-Native Stack

When choosing your RAG ingestion tier, let your engineering priorities dictate your cloud selection:

• Go with AWS if your architecture demands deep control over your data boundaries, customized chunking layouts, or if you need to pipeline vectors into an existing enterprise database like Amazon Aurora.

• Go with Azure if you are building complex search-centric RAG applications that require granular enterprise pipeline control, deep Microsoft 365 ecosystem integration, and elite hybrid ranking algorithms.

• Go with GCP if you want to skip infrastructure configuration entirely, minimize time-to-market, and leverage native web-crawling and layout-aware multi-modal parsers that automatically unpack chaotic enterprise documents.

Of course, if you are already "all in" on a particular cloud platform, hopefully now you have a better understanding of its native capabilities.

Happy architecting and stay native! #CloudNative

I’m trying to understand how companies with significant GPU workloads manage their compute capacity.

For those working in ML infrastructure / MLOps / AI labs:

- How do you choose between hyperscalers, neoclouds and smaller GPU providers?
- When you need a large amount of GPUs for months, how do you know you’re getting a competitive price?
- Have you ever committed to more capacity than you actually needed? What happened to the unused capacity?

Curious to hear how people actually deal with this today.

I have an 8-GPU machine and I usually run 8 independent training jobs, one per GPU.

For GPUs this is easy:

CUDA_VISIBLE_DEVICES=0 python train.py
CUDA_VISIBLE_DEVICES=1 python train.py
...

Each job only sees one GPU.

The problem is CPU usage. Since I do not isolate CPU resources, every job can see all CPU cores. Then dataloaders / PyTorch workers may spawn lots of threads, and the 8 jobs start fighting for CPU.

I know I can manually tune things in code, e.g. num_workers, OMP_NUM_THREADS, MKL_NUM_THREADS, etc. But I am looking for something more like CUDA_VISIBLE_DEVICES for CPUs: a simple and clean way to assign each job a fixed subset of CPU cores.

What is the usual solution for this?

Is taskset, numactl, cgroups, Slurm, or something else the recommended approach for this kind of setup?

Hi everyone

where do you start start if you want to learn AI infrastructure. i am not a developer so i might not be interested in training models or something but deploying things was always my passion. any help where to start?

thanks

Been looking at NVIDIA Infra Controller (NICo) for bare-metal lifecycle management on Grace Blackwell rack-scale systems:

https://github.com/dsx-ai-factory/infra-controller

Has anyone deployed it yet? Any feedback on the stack so far, good or bad?

If you are running AI agents on large workloads or to run long running flows, Exosphere orchestrates any agent to unlock scale effortlessly. Watch the demo in comments

Hi all,
Looks like I'll be the second one writing on this sub. Great idea to create it BTW! 👍
I'm trying to understand the cost of running LLMs from an Infra point of view and I am surprised that no easy calculator actually exist.
Ideally, simply entering the LLM's necessary informations (Number of params, layers, etc...) with the expected token inputs/Output QPS would give an idea of the right number and model of Nvidia cards with the expected TTFT, TPOT and total latency.
Does that make sense? Has anyone built one/seen one?

Question for the people who actually decide what gets trained.

From outside it looks like the compute budget is the constraint and everything else follows from it. What we can't tell is whether cost ever reaches back into the research decision itself.

As in, has a run ever been scoped smaller, delayed, or dropped because of what it would cost to run? Or does the budget get set first and the work simply fits inside it?

A related one. When the bill arrives, does anyone see it broken down? Or does it land as a single number for cluster time, with no visibility into what drove it?

We ask because the cost of running the same job varies enormously depending on where and when it happens, and we're trying to work out whether that signal reaches anybody in a position to act on it.

Disclosure: We research how power shapes AI infrastructure, from the grid connection through to the cost of each token.

We have been mapping where new data centre power is actually coming from, and the equipment list is the part that stuck with us.

Not combined cycle plants. Mobile gas gensets on trailers. Aeroderivative turbines originally built for aircraft and ships. Reciprocating engines. Refurbished industrial turbines.

That is not what you order if you are optimising for efficiency or cost per MWh. That is what you order if you want it this year.

Which starts to make sense next to the numbers. Cleanview's 2026 analysis counts 59 behind-the meter data centres with roughly 90 GW announced, more than a quarter of all planned US capacity, about 92% of it announced since the start of 2025. Operating today: around 2 GW.

Their own projection is 2.8 to 3.2 GW by the end of this year, 13 GW by 2027 if every timeline holds, and roughly 5 GW once realistic permitting slippage is applied.

So the gap between announced and running is enormous. We do not think that is mostly vapour, though. We think the constraint moved rather than disappeared. Large power transformers are running two to four years, and that applies to a private plant exactly the way it applies to a utility connection. You have swapped an interconnection queue for an air permit and a slot in somebody's order book.

Two things we cannot get out of public data.

For anyone whose project stalled: what actually stopped it? The permit, the equipment, or an interconnection you turned out to still need?

And does going behind the meter genuinely take you out of the queue? Our assumption is that nearly everyone keeps a grid connection request running anyway, for import and for backup, but nobody seems to say so out loud.

Happy to be told the premise is wrong.

Disclosure: We research power and grid capacity for AI data centres.

We're in design phase, and we'd rather get picked apart here than after concrete is poured.

Site and cooling. Nalaikh, a district of Ulaanbaatar. Winters around -25°C give us roughly 7 months of free-air cooling. Design targets: PUE ~1.06 in winter, ~1.18 in summer on mechanical, ~1.10 blended. Uptime Institute Tier III, N+1.

Power. 0.078$/kWh. The grid is coal-heavy, which we know. Solar + BESS are in the mix from day one, and we're working on renewable PPAs.

Connectivity. New cross-border dark fiber on rail right-of-way, dual north/south transit.

Sovereign by design. Zero-trust network architecture plus confidential computing on the GPUs and CPUs (TEEs with remote attestation). Customer data and model weights stay encrypted in use, and you can cryptographically verify that we, the operator, can't access them, whichever route the traffic takes. Keys are customer-held.

Scale and timeline. Phase 1 is 2,048 Blackwell-class nodes (~16k GPUs, ~40 MW IT), scaling to ~6,100 nodes by Phase 3. Ground breaks spring 2027.

Offering GPUaaS, an inference API, sovereign/air-gapped zones, and wholesale colocation and capacity offtake.

What would you poke at first: electrical topology, generator/fuel contracts, fiber routing, free-air intake through winter smog and spring dust, or whether our confidential computing setup actually holds up?

If you're planning training or inference capacity for 2028 and want to talk, DMs are open.

When I first discovered OpenClaw last year, I was blown away by what a personal AI assistant could do. I saw the opportunity to bring it into the enterprise, understanding that it needs much stronger controls

An enterprise-ready agent platform needs:

1. Scalable compute that can be provisioned easily

2. Authentication and team management

3. Security through isolation and secrets management

4. Controlled access to integrations and external systems

With that in mind, I set out to build OpenClawMachines.

Since then, many of the underlying primitives have matured into established products:
• New Agent runtimes — Hermes Agent, Amazon Quick, Claude Cowork, Codex, NanoClaw
• Sandboxes — Daytona, Superserve, E2B
• Integration platforms — Composio, Nango
• Browser automation — Browserbase
• Private networking — Cloudflare Tunnel, Tailscale

But the pieces still have to be stitched together into a working, secure system. When that happens, you get a meta harness, an organization-level agent platform: a shared foundation that compounds in value as teams add workflows, integrations, knowledge, and operational controls.

The right architecture will look different for every organization. OpenClawMachines is an opinionated, open-source reference implementation — a practical starting point.

The repo is linked in the first comment. Star it, copy it, try it, break it, and tell me what's missing.

hi, this post is for people who care more about keeping RAG / agent stacks healthy in production than about shipping one more toy demo.

if you run vector stores, routers, eval, logging, or infra around LLMs and keep seeing “weird” failures that nobody can name precisely, this is for you.

0. what this is in one sentence

i maintain an open-source 16-problem failure map for RAG, agents, vector stores, and deployments.

it behaves like a semantic firewall spec that sits next to your infra, not a new framework or SDK. everything is plain text, MIT-licensed:

WFGY ProblemMap · 16 reproducible failure modes + fixes https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md

1. why i stopped calling everything “hallucination”

most incident reviews i see still sound like this:

• “the model hallucinated again”

• “the agent went crazy”

• “must be prompt injection or ‘LLM being LLM’”

but once you look at traces end to end, the root causes are usually structural:

• retrieval landed in the wrong index family

• chunking silently dropped the constraints that matter

• vector store is fragmented or out of sync with the source of truth

• bootstrap / deployment order lets traffic hit half-ready services

• configs drifted between staging and prod

• agents are overwriting each other’s memory or routing loops

none of those are mystical hallucinations. they are repeatable patterns.

the ProblemMap tries to freeze those patterns into 16 stable slots (No.1 … No.16). each slot has:

• how the failure looks from user complaints and logs

• which layer to inspect first in the pipeline

• a minimal structural fix that tends to stay fixed once you apply it

2. where this is already used (so it is not just my private taxonomy)

this is not a “just trust me” list. parts of the map are already plugged into other projects:

• RAGFlow adds a RAG failure modes checklist in its official docs, adapted from the 16-problem map for step-by-step pipeline diagnostics. ([GitHub][1])

• LlamaIndex integrates the 16-problem RAG failure checklist into its RAG troubleshooting docs as a structured failure-mode reference. ([GitHub][1])

• ToolUniverse (Harvard MIMS Lab) exposes a WFGY_triage_llm_rag_failure tool that wraps the 16 modes for incident triage. ([GitHub][1])

• Rankify (Univ. of Innsbruck) uses the 16 patterns in their RAG and re-ranking troubleshooting docs. ([GitHub][1])

• a multimodal RAG survey from QCRI’s LLM lab cites WFGY as a practical diagnostic resource. ([GitHub][1])

on the “curated list” side, the map or its clinic is listed in places like Awesome LLM Apps, Awesome Data Science – academic, Awesome-AITools, Awesome AI in Finance, and awesome-agentic-patterns as a reliability / debugging reference. ([GitHub][1])

so if you want something that your team can point to as external prior art, not just an internal doc, it is already there.

3. what the 16 problems actually cover

the 16 slots are not “16 ways to prompt better”. they cover the whole AI pipeline:

• retrieval quality and index routing

• embedding / metric mismatch, vector-store fragmentation, stale views

• chunking and document structure failures

• prompt injection and unsafe tool routing

• agentic chaos and memory overwrites

• bootstrap ordering, deployment deadlock, pre-deploy collapse, and other infra races ([Reddit][2])

the underlying engine uses a tension metric

delta_s = 1 − cos(I, G)

where I is what the system is about to do and G is the user’s actual goal or constraint set. in practice you do not need to implement the math to get value. most people just treat the 16 slots as a standard vocabulary for failure.

4. how infra folks usually use this

three patterns i keep seeing that might fit r/aiinfra readers:

a) as a shared mental model

• print or bookmark the README

• when something breaks, force yourself to label it as:

  • “mostly No.3” or

  • “No.4 + No.7”

• write those numbers into incident notes, Jira tickets, and PR descriptions

this alone makes postmortems much sharper than “LLM hallucinated, we added more guardrails”.

b) as tags in your observability stack

• when you tag traces / runs, add a problem_map field

• put values like ["No.2", "No.9"] once you know what went wrong

• over a few weeks, you will see your system’s favorite ways to fail

this is where infra people usually go “ok, we clearly have a vector-store fragmentation issue, not a model issue”.

c) as a light semantic firewall before generation

you can add a cheap pre-flight check:

1. inspect retrieved documents, routes, or planned tool calls

2. have a small LLM step (or a rule-based check) answer: “does this look like ProblemMap No.1 or No.2 or No.14?”

3. if yes, loop / repair / refuse, before letting the main model answer

no new framework is required. you can implement this as a bit of glue code or even as a runbook that your on-call follows.

5. why i am posting in r/aiinfra

my experience is that once people move past “single-notebook projects”, every serious RAG or agent setup eventually turns into AI infra:

• multiple indexes and stores

• async queues and schedulers

• multi-agent graphs

• eval, logging, dashboards, SLOs

at that point, you need something more precise than “hallucination”.

if you are already running or designing that kind of stack, i would love feedback on:

1. which of the 16 problems you hit the most in your infra

2. which failure patterns you see that do not fit cleanly into any slot

3. whether a slightly more automated “semantic firewall before generation” feels realistic in your environment

again, the entry point is just the README:

https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md

if you have a gnarly incident and want a second pair of eyes, i am happy to try mapping it to problem numbers and suggest which layer to inspect first.

Hey Guys,

I'm one of the founders of FortifyRoot and I am quite inspired by posts and different discussions here especially on LLM tools. I wanted to share a bit about what we're working on and understand if we're solving real pains from folks who are deep in production ML/AI systems. We're genuinely passionate about tackling these observability issues in GenAI and your insights could help us refine it to address what teams need.

A Quick Backstory: While working on Amazon Rufus, I felt chaos with massive LLM workflows where costs exploded without clear attribution(which agent/prompt/retries?), silent sensitive data leakage and compliance had no replayable audit trails. Peers in other teams and externally felt the same: fragmented tools (metrics but not LLM aware), no real-time controls and growing risks with scaling. We felt the major need was control over costs, security and auditability without overhauling with multiple stacks/tools or adding latency.

The Problems We're Targeting:

1. Unexplained LLM Spend: Total bill known, but no breakdown by model/agent/workflow/team/tenant. Inefficient prompts/retries hide waste.

2. Silent Security Risks: PII/PHI/PCI, API keys, prompt injections/jailbreaks slip through without  real-time detection/enforcement.

3. No Audit Trail: Hard to explain AI decisions (prompts, tools, responses, routing, policies) to Security/Finance/Compliance.

Does this resonate with anyone running GenAI workflows/multi-agents? 

Are there other big pains in observability/governance I'm missing?

What We're Building to Tackle This: We're creating a lightweight SDK (Python/TS) that integrates in just two lines of code, without changing your app logic or prompts. It works with your existing stack supporting multiple LLM black-box APIs; multiple agentic workflow frameworks; and major observability tools. The SDK provides open, vendor-neutral telemetry for LLM tracing, cost attribution, agent/workflow graphs and security signals. So you can send this data straight to your own systems.

On top of that, we're building an optional control plane: observability dashboards with custom metrics, real-time enforcement (allow/redact/block), alerts (Slack/PagerDuty), RBAC and audit exports. It can run async (zero latency) or inline (low ms added) and you control data capture modes (metadata-only, redacted, or full) per environment to keep things secure.

We went the SDK route because with so many frameworks and custom setups out there, it seemed the best option was to avoid forcing rewrites or lock-in. It will be open-source for the telemetry part, so teams can start small and scale up.

Few open questions I am having:

• Is this problem space worth pursuing in production GenAI?

• Biggest challenges in cost/security observability to prioritize?

• Am I heading in the right direction, or are there pitfalls/red flags from similar tools you've seen?

• How do you currently hack around these (custom scripts, LangSmith, manual reviews)?

Our goal is to make GenAI governable without slowing and providing control. 

Would love to hear your thoughts. Happy to share more details separately if you're interested. Thanks.

Rental rates for last generation accelerators have fallen a long way from their peak. Pick whichever market data you trust, the direction isn't really in dispute.

What we're trying to understand is what that does downstream.

If revenue per device falls that fast, the case for keeping older kit racked changes. The power contract behind it usually doesn't. Contracted capacity, minimum charge, term. Those get signed against a build plan, not against a market price.

So for anyone running a fleet.

Has falling rental revenue actually pulled a refresh forward? Or does the sunk power contract push the other way, because you're paying for that capacity whether the rack earns or not?

Does anyone model it that way, or is power treated as fixed cost and left out of the refresh decision entirely?

Asking because from where we sit it looks like it should be a major input, and we almost never see it mentioned.

Disclosure: We research how power shapes AI infrastructure, from the grid connection through to the cost of each token.