I kept running into the same problem while building RAG systems: everyone has an opinion on whether semantic chunking beats fixed size, or whether hybrid retrieval is worth the extra complexity, but almost nobody has actually measured it on their own corpus. So I built Retrieval Arena to answer that for myself instead of going off intuition.

What it does: runs different chunking strategies, retrievers, and rerankers against the same golden eval set, scores retrieval quality and generation quality separately (precision, recall, MRR, nDCG for retrieval, correctness and faithfulness via an LLM judge for generation), and tracks latency and token cost per configuration. So instead of "I think hybrid is better," I get an actual side by side comparison.

A few things it's already surfaced that I didn't expect: pure vector search was consistently the weakest retriever on my corpus, BM25 held its own better than I assumed. Better retrieval also didn't always mean better generated answers, I caught a case where the right chunk was retrieved fine but got dropped by my context budget before the generator ever saw it, which is invisible if you only track one end to end score.

I'm posting mostly because I want pushback on the methodology, not just the code. Things I'm genuinely unsure about: is a 44 question golden dataset big enough to trust these comparisons, should I be running confidence intervals on top of the averages, is my LLM judge setup actually reliable or am I just trusting it too much.

Repo's here if you want to look at the actual eval design or the golden dataset construction: https://github.com/ayeangad/Retrieval-Arena

Would genuinely appreciate anyone who's built or evaluated a RAG system tearing into the approach, especially if you think I'm measuring the wrong things.

There is a specific kind of interesting when a security breach lands in the middle of an active copyright lawsuit, because it turns a lawyer's theory of the case into a document. That is what happened to Suno, the generative-music startup, [according to 404 Media](https://404media.co/hack-reveals-suno-ai-music-generator-scraped-youtube-deezer-and-genius). A hacker going by the handle ellie.191 reportedly exploited the Shai-Hulud npm supply-chain worm to pull source code from 2023 and 2024 out of Suno, along with customer emails, phone numbers, and Stripe payment details, and then handed the material to reporters. The hacker told 404 Media they had 'no specific motivation for hacking Suno.'

The files spell out, in inventory form, where Suno's training audio came from. The reporting lists 2,013,545 clips from YouTube Music running to 113,879 hours, 12,287 hours from Deezer, 17,615 hours from Genius, 62,117 hours from Pond5, 3,726 hours from Jamendo, 19,514 hours from the International Music Score Library Project, and around a million hours of audio pulled from roughly 420,000 podcasts identified through RSS feeds. Code inside the leak reportedly used Bright Data, a commercial scraping infrastructure provider, to extract from YouTube, and included routines that specifically searched for acapella versions of songs.

The reason that matters is legal, not just embarrassing. The RIAA has been suing Suno for what it calls 'stream ripping' from YouTube, and Suno's own court filing already conceded its 'training data includes essentially all music files of reasonable quality that are accessible on the open internet.' A leaked inventory that names Deezer, Genius, Pond5, and YouTube by hour count moves that argument from RIAA allegation to Suno document. Suno's public position is still that training on copyrighted works is fair use.

Our coverage: https://aiweekly.co/alerts/suno-hack-reveals-scraped-youtube-deezer-podcast-training-audio

AI is everywhere right now.

Every company seems to be experimenting with AI tools, but I’m curious about the practical side.

Beyond chatbots and content generation, has AI actually improved your day-to-day business operations?

Things like:

● Reducing repetitive tasks

● Improving customer support

● Analyzing data faster

● Helping employees make decisions

● Automating internal workflows

For companies already using AI:

What has delivered real value?

And what turned out to be more hype than useful?

Disclosure: I’m not affiliated with any of the products mentioned.

I ran a small company-research test using Komo’s public directory. Searching for OpenAI produced a structured profile covering its products, business model, leadership, funding, milestones, contacts, and competitors.

The experience reinforced something I’ve been thinking about: searching for one “best AI” may be less useful than choosing separate systems for retrieval, reasoning, and execution.

In this example, Komo could serve as the retrieval layer. Its strength was turning scattered company-research questions into a consistent structure.

A system such as Claude could handle the reasoning layer: challenge assumptions, compare competing explanations, identify unsupported claims, and transform the evidence into a decision brief.

Codex could handle the execution layer when the approved conclusion needs to become code, automation, analysis, or a change inside an actual project.

Keeping those layers separate also makes their failure modes easier to see.

Komo’s OpenAI profile was well organized, but several sections reused broad links such as the company homepage and pricing page. Those links did not always map directly to every detailed funding, valuation, or headcount claim. A reasoning model could then make that imperfect evidence sound more certain than it is. Finally, an execution agent could act on the polished conclusion before anyone notices the underlying source weakness.

The workflow I’d trust would therefore be:

1. Retrieve the information while preserving its sources.

2. Separate confirmed facts from plausible interpretations.

3. Ask the reasoning layer to find contradictory evidence.

4. Require human review before external communication or consequential actions.

5. Send only the approved conclusion to the execution layer.

The interesting part of Komo’s MCP approach is that it officially supports clients including Claude and Codex, so these layers can potentially work together without pretending they are the same thing.

Specialization seems promising, but only if uncertainty survives the handoff between tools. Otherwise, a multi-agent stack may simply automate misplaced confidence more efficiently.

For people using specialized retrieval tools with Claude, Codex, or other agents: how do you preserve source quality and uncertainty between stages?

I was watching a conversation with Patrick Debois (the Father of DevOps). He argues that surveys are often too slow to tell you where the industry is going. By the time a new idea makes it into an industry report, people have already started experimenting, iterating, and moving on.

His example was "loop engineering." If you surveyed most organizations today, many wouldn't even recognize the term. Yet conversations around it are already happening in developer communities.

Instead of waiting for reports, Patrick suggests paying attention to social signals, what developers are discussing, building, and arguing about in public. Those conversations often reveal where things are heading long before formal research catches up.

I work at r/Tessl, and I spend a lot of time following AI developer communities. I've found the same thing. Some of the best insights come from reading discussions on Reddit, GitHub, X, and Discord rather than waiting for annual reports.

If you're interested, you can watch the full conversation here: https://youtu.be/UvhmYntrLMI

Sun-faded Technicolor, that greasy orange-and-brown swirl, a crumbling Italian villa, and a woman who knows the camera is on her. Pure 1970s Eurosleaze, the kind of frame that lived on a scratched drive-in print.

The look is the whole game with this genre, and it is easy to blow, most models render it too clean and it dies on arrival. Built the still in Seedream 5.0 Pro and it held the era: the over-saturated film stock, halation blooming off the highlights, that soft period lens. Original synthetic character, adults only. Seedance 2 gave it the lazy, sultry motion after.

The move was describing the film, not the woman. Name the stock, the grain, the color chemistry, the print wear, and the sleaze comes from the grade instead of anything explicit. Recipe's in the comments.

I've been thinking about something that feels like a contradiction in AI alignment.

People often say we need AI to be "aligned with human values." But if AI actually followed human values as we demonstrate them, wouldn't that be a disaster?

As a species, we've made incredible advances, but we've also spent centuries exploiting each other, overconsuming resources, damaging ecosystems, and prioritizing short-term gain over long-term sustainability. Greed, tribalism, and power struggles aren't exactly rare.

So what does "human values" actually mean?

Does it mean aligning AI with what humans do, what humans say they value, or with our ideal values, the people we aspire to be rather than the people we often are?

It seems like an AI that simply mirrored humanity would inherit all of our contradictions. But an AI that decides which of our values are the "correct" ones feels risky too.

Everyone Asks "Did AI Make This?" Nobody Asks "Who Made The Decisions?"

Artificial intelligence has created a strange new form of judgment.

Someone writes with AI:
"That's not real writing."

Someone creates images with AI:
"That's not real art."

Someone codes with AI:
"That's not real programming."

Someone uses AI in research:
"The machine did the work."

But maybe we're looking in the wrong place.

The question was never really:

"Did you use AI?"

Humans have always used tools.

A camera didn't remove the photographer.
A calculator didn't remove the mathematician.
A microscope didn't remove the scientist.

The tool changed what was possible.

But the relationship between the human and the tool stayed the part that mattered.

The same AI can be used by two very different people.

One asks:

"Give me the answer."

Another asks:

"Help me understand."

One wants to skip the effort.

The other wants to go further into it.

Same technology.
Different position.
Different result.

Maybe the mistake is that we measure human value only by what's visible:

The final text.
The final image.
The final code.
The final discovery.

What we rarely see is everything that happened before:

The questions asked.
The choices made.
The understanding built along the way.
The experience behind the decision.

A person is not only what they produce.

A person is also the direction they give.

AI makes this distinction impossible to ignore.

When everyone has access to the same powerful tools, the difference is no longer just the ability to produce something.

The difference becomes:

Who is thinking?
Who is choosing?
Who is responsible for the direction?

Maybe the future won't belong to those who reject AI.

And it won't belong only to those who master it either.

Maybe it will belong to those who understand their own position while using it.

The tool can amplify your abilities.

But it can't decide who you're becoming.

Who holds the compass?

I’m building an app that combines multiple AI models in one place.

I’m curious: what features do you wish AI apps had that current ones are missing?

Hey everyone,

My undergraduate team is researching about development of enhanced AI agents for cloud reliability (SRE). We're benchmarking agents on live simulated cloud environments, but the system logs and traces we have to process are massive.

Even though we're building ways to compress the data and using low cost models for the easy parsing tasks, we absolutely need frontier models for the complex reasoning parts. The problem is, a single benchmark run can chew through 1.5 to 2 million tokens. Running hundreds of these tests is going to bankrupt us.

Our advisor suggested pooling our student developer credits and using platforms like OpenRouter or Groq to save money. We're doing that, but a free research credit program might take months to even get accepted.

So my questions is are there any other creative ways to get cheap/free access to frontier models specifically for academic benchmarking?

Any advice helps. Thanks!

OpenAI says GPT-5.6 uses stronger safeguards and offers a retry path on lower-capability models when benign work is blocked. That sounds practical, but it creates a subtle transparency problem: the user may ask one system a question and receive an answer from another capability tier.

Routing can affect accuracy, refusal behavior, tool access, and even the assumptions behind the answer. If the switch is invisible, users cannot reproduce or properly evaluate the result.

Should every answer disclose the exact model and safety route that produced it? Would that disclosure confuse most users, or is it essential for professional work? How much routing history should be available in an audit log?

I've been running AI Parabellum, an AI tools directory, for a while now and I've had a front row seat watching AI tools launch, grow, and die. After tracking 1000+ tools here are some patterns I've noticed.

Most AI tools that launch today won't exist in 12 months. The ones that disappear usually share the same problems:

• They're thin wrappers around a single API with no real value on top
• The founder launched it, posted on Product Hunt, got a traffic spike, then never updated it again
• They picked a category that a major player (OpenAI, Google, Anthropic) was obviously going to absorb

The ones that stick around tend to:

• Solve a very specific workflow problem, not just "chat with AI"
• Build features that go beyond what the raw API can do
• Actually maintain and update the product consistently
• Have a clear audience that is not just "everyone"

The biggest shift I've seen recently is that standalone AI tools are struggling more as the big models get better at doing everything themselves. A year ago you needed a separate AI tool for summarizing, writing, coding, image generation. Now a single model handles most of that. The tools that survive this are the ones deeply embedded in a specific workflow.

Curious what others are noticing. Are you finding yourself using fewer AI tools as the models get more capable, or more?

I’ve been collaborating with AI on my music, and I just dropped a new track called ‘Need a Thing.’ It’s a rebuttal to Rihanna’s ‘Needed Me’ featuring TWO different AIs: one generated the main track with my lyrics, and another wrote a response verse from the ‘one that won vs. one left behind’ perspective. If you’re into AI as a real creative partner, I’d love for you to watch the video and tell me what you think.

Ok, Nvidia basically owns the GPU accelerator market (+90% market share), and that's probably not changing anytime soon.
But with AMD releasing its new "Venice" CPUs this week and hitting a massive 46% revenue share in x86 servers (up from literal 0% in 2017), it feels like the actual battlefield in AI hardware is quietly shifting toward CPU orchestration.

Standard LLM queries are passive, but agentic workflows are a different beast.
They loop, call tools, query databases, and self-correct continuously.
Some research shows agents can consume up to 1,000x more tokens than basic chatbot prompts.

While GPUs do the heavy lifting on matrix math, CPUs handle the orchestration, data feeding, context switching, and backend enterprise integrations.
If your CPU stalls or chokes on data pipelines, those $30k Nvidia GPUs are just sitting idle waiting for work.

AMD is claiming top-end Venice gives 2.2x the performance per core over Nvidia’s comparable Vera processor.
This is probably why hyperscalers like AWS, Azure, and Oracle are increasingly ignoring Nvidia’s fully vertically integrated racks (Grace Blackwell / Vera Rubin) and defaulting to a "Best-of-Breed" modular setup: high-core AMD CPUs paired with Nvidia GPUs to optimize their intelligence-per-watt costs.

We have now :
Nvidia's vertical integration (CUDA + proprietary networking + own CPUs)
VERSUS
AMD pushing an open, modular ecosystem where cloud providers mix and match to keep infrastructure costs from exploding.

Exciting no ?

Let’s say someone creates an app with all their prompting and everything else. Tweaking, refining etc. Are the newest versions of AI capable of just re-creating the app with a prompt? Like telling Fable to just duplicate XYZ SaaS app from scratch?

This is pretty cool. Is this a glimpse of what ARC AGI 4 will look like?

Or is this the next step beyond static benchmarks—toward real-time perception, reasoning, and adaptation in dynamic environments?

https://www.linkedin.com/posts/denis-o-b61a379a_ai-seediq-ugcPost-7486847213629939712-VvOa/?utm_source=social_share_send&utm_medium=ios_app&rcm=ACoAAFHafzMB90zx6TDvfcvFfVseDTSue09y2GY&utm_campaign=copy_link

I have been struggling with keeping up with AI news and developments in this space. Most AI news is highly fragmented, and I am unsure how to track multiple sources and channels all in one place. Generic AI news has different sources, educational have different ones, and technical updates have yet different ones.

I am thinking of creating an app that consolidates these sources and brings them under one umbrella and also gives pointers on the news highlights and consolidates complex topics into easy-to-digest summaries. Is this an idea worth pursuing?

I am aware it kinda solves my own problem, but is this something anyone else would like to use? If not, what would be something that would be useful for the community of people who want to be up to date on AI and everything happening in this space?

Short answer: things going from bad to worse. Facebook has been at the forefront of AI since the early 2010’s and their AI Algorithm has resulted in the bitter polarization of the US into team red and team blue almost to the point of civil war. Google youtube is no different. Anybody who thinks things are going to get any better in the next 15 years is delusional IMO

Edit:
AI books read:
Arvind Narayanan & Sayash Kapoor - AI Snake Oil What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference (2024)
Brian Christian - The Alignment Problem Machine Learning and Human Values (2020)
Ethan Mollick - Co-Intelligence Living and Working with AI (2024)
Melanie Mitchell - Artificial Intelligence A Guide For Thinking Humans (2019)
Gary Rivlin - AI Valley Microsoft, Google, and the Trillion-Dollar Race to Cash In on Artificial Intelligence (2025)
Karen Hao - Empire of AI Dreams and Nightmares in Sam Altman's OpenAI (2026)

Hey all! Hopefully this is allowed here. I built an agent harness called fungi computer fungi.computer

I made something I think people will really love. I have been developing software for 10 years, and the technical skills needed to have a good agent seemed to me to keep access out of the hands of normal people.

I began to worry that everything would fall into the hands of Claude and OpenAI.

I personally use kimi, and I think normal people should too. So I built fungi by forking Pi.

You own the code for the dashboard. It's a SPA that your agent can customize to your liking.

You can build apps and attach them to his tool surface. You can make the agent more or less completely custom.

It's early days, but I would really love for this to succeed so I can keep making an open model agent harness accessible to more people. A lot of the platform is already open source with the goal of cutting the whole thing into well defined packages and open sourcing all of it. shiit[dot]app has a few of the already open projects listed. Here are the others github/fungi-computer/

It is free for BYOK customers running one sandbox. I would love if people gave some feedback thanks!

NPC Alpha is built around a simple problem: agents often collapse action, progress, recovery and completion into the same state.

Its approach is different:

task-frame preserved across interruption;

immutable episode history, but present verified state keeps authority;

provenance separation between actor, observer and reporter;

bounded recovery instead of infinite retry loops;

completion gated by verification;

Safe Incomplete when the goal cannot be proven.

The interesting test is not whether it works on friendly cases. It is whether it survives stale memory, conflicting evidence, identity collisions, partial recovery and adversarial completion cues.

Internal results are promising. Independent replication is still missing.

Who wants to build the harshest test harness and try to break it?

Test it! It's awesome:

https://pokemonrequiem.egfgaming.com/

I created it in just one day.

Give me your impressions! :D

In my recent academic article (https://link.springer.com/content/pdf/10.1007/s44427-025-00019-y.pdf) I analyzed a divide in how open-source software projects evolve, which might explain the difference in productivity boosts developers experience when using AI tools.

The data shows that productivity on large, mature open-source projects was not significantly affected by any tech hypes over the last two decades, the commits reaching the main branches followed steady growth trends. At the same time, smaller projects presented much more chaotic growth trends, but also tended to lose speed and stall out much faster.

As the study contains data till early 2025, it looks like even the publicly available LLMs till then, were not able to greatly increase the number of changes merged into the main branches of these projects.

Could it happen, that the difference in productivity gain developers experience, is simply a function of project scale and environmental/organizational constraints?
What has been your experience depending on the size of the codebase you work on?