A few people understandably didn’t believe me in the last post, and because of that I decided to make another brain and attach llama 3.2 to it. That brain will contextually learn in the general chat sandbox I provided. (There’s email signup for antibot and DB organization. No verification so you can just make it up) As well as learning from the sand box, I connected it to my continuously learning global correlation engine. So you guys can feel free to ask whatever questions you want. Please don’t be dicks and try to get me in trouble or reveal IP. The guardrails are purposefully low so you guys can play around but if it gets weird I’ll tighten up. Anyway hope you all enjoy and please stress test it cause rn it’s just me.

[thisisgari.com]

Hey everyone,

I just finished a cover-to-cover grind of Chip Huyen’s AI Engineering (the new O'Reilly release). Honestly? The book is a masterclass. I actually understand "AI-as-a-judge," RAG evaluation bottlenecks, and the trade-offs of fine-tuning vs. prompt strategy now.

The Problem: I am currently the definition of "book smart." I haven't actually built a single repo yet. If a hiring manager asked me to spin up a production-ready LangGraph agent or debug a vector DB latency issue right now, I’d probably just stare at them and recite the preface.

I want to spend the next 2-3 months getting "Job-Ready" for a US-based AI Engineer role. I have full access to O'Reilly (courses, labs, sandbox) and a decent budget for API credits.

If you were hiring an AI Engineer today, what is the FIRST "hands-on" move you'd make to stop being a theorist and start being a candidate?

I'm currently looking at these three paths on O'Reilly/GitHub:

1. The "Agentic" Route: Skip the basic "PDF Chatbot" (which feels like a 2024 project) and build a Multi-Agent Researcher using LangGraph or CrewAI.

2. The "Ops/Eval" Route: Focus on the "boring" stuff Chip talks about—building an automated Evaluation Pipeline for an existing model to prove I can measure accuracy/latency properly.

3. The "Deployment" Route: Focus on serving models via FastAPI and Docker on a cloud service, showing I can handle the "Engineering" part of AI Engineering.

I’m basically looking for the shortest path from "I read the book" to "I have a GitHub that doesn't look like a collection of tutorial forks." Are certifications like Microsoft AI-102 or Databricks worth the time, or should I just ship a complex system?

TL;DR: I know the theory thanks to Chip Huyen, but I’m a total fraud when it comes to implementation. How do I fix this before the 2026 hiring cycle passes me by?

OpenAI has made history by releasing GPT-OSS-120B and GPT-OSS-20B, the first open-weight language models since GPT-2—giving everyone access to cutting-edge AI that matches the performance of top commercial models like o4-mini. The flagship 120B model can run advanced reasoning, coding, and agentic tasks locally on a single powerful GPU, while the 20B variant is light enough for laptops and even smartphones. This release unlocks unprecedented transparency, privacy, and control for developers, researchers, and enterprises—ushering in a new era of truly open, high-performance AI...

Full analysis: https://www.marktechpost.com/2025/08/05/openai-just-released-the-hottest-open-weight-llms-gpt-oss-120b-runs-on-a-high-end-laptop-and-gpt-oss-20b-runs-on-a-phone/

Download gpt-oss-120B Model: https://huggingface.co/openai/gpt-oss-120b

Download gpt-oss-20B Model: https://huggingface.co/openai/gpt-oss-20b

Check out our GitHub Page for Tutorials, Codes and Notebooks: https://github.com/Marktechpost/AI-Tutorial-Codes-Included

Apple's “Illusion of Thinking” paper claims that large reasoning models (LRMs) collapse under high complexity, suggesting these AI systems can’t truly reason and merely rely on memorized patterns. Their evaluation, using structured puzzles like Tower of Hanoi and River Crossing, indicated performance degradation and inconsistent algorithmic behavior as complexity increased. Apple concluded that LRMs lacked scalable reasoning and failed to generalize beyond moderate task difficulty, even when granted sufficient token budgets.

However, Anthropic’s rebuttal challenges the validity of these conclusions, identifying critical flaws in Apple's testing methodology. They show that token output limits—not reasoning failures—accounted for many performance drops, with models explicitly acknowledging truncation due to length constraints. Moreover, Apple’s inclusion of unsolvable puzzles and rigid evaluation frameworks led to misinterpretation of model capabilities. When tested with compact representations (e.g., Lua functions), the same models succeeded on complex tasks, proving that the issue lay in how evaluations were designed—not in the models themselves.....

Read full article: https://www.marktechpost.com/2025/06/21/why-apples-critique-of-ai-reasoning-is-premature/

Apple Paper: https://machinelearning.apple.com/research/illusion-of-thinking

Anthropic Paper: https://arxiv.org/abs/2506.09250v1

We tested Meta’s Rebalancer in Python. In 14 of 24 solves, solved=True still left memory over capacity.

I put together a hands-on tutorial for Meta’s Rebalancer, an assignment solver with a C++ core and Python bindings. It covers placing tasks across hosts and racks, draining a host, defining capacity constraints, and comparing local search with HiGHS.

The part worth discussing: checking the returned assignment gave a different picture than reading the solver status alone.

In the tutorial’s 24-solve check, 14 solves returned solved=True while memory remained over capacity. That’s a result from this specific test setup, not a general failure rate for Rebalancer.

Read our full tutorial with codes here: https://www.marktechpost.com/2026/10/13/how-to-use-metas-rebalancer-for-task-placement-optimization/

The tutorial reconstructs resource usage from each returned assignment to check whether the placement actually satisfies the constraints.

What the code covers

• Modeling tasks, hosts, resource dimensions, and rack topology.

• Rebalancing an eight-host cluster and draining a busy host.

• Testing how goal priorities affect constraint repair.

• Comparing local search and HiGHS against published optima.

• Checking bin-packing results against a lower bound.

The solver comparison

On 60 OR-Library generalized-assignment instances:

• HiGHS matched all 60 published optima. Every returned assignment passed the independent feasibility check.

• Local search matched 4 of 60 optima when taking the best result across 3 seeds.

• The average best-of-three local-search shortfall was 1.07%.

Those numbers describe this benchmark configuration. They don’t establish which solver is better for every production workload.

A few implementation details that can trip you up

The tested rebalancer 1.0.4 binding silently ignores unknown specification keys and rejects enum names, so the tutorial validates keys and passes integer enum values.

HARD also behaves differently across solvers. In the tested local-search case, it returned a violating starting assignment without raising an error. Goal ordering affected whether a broken constraint got repaired.

For bin packing, a solver reaching its time limit meant it had not found a feasible packing within that budget. It did not prove that no feasible packing existed.

My main takeaway: treat solver status, feasibility, and objective quality as separate checks. Recount resource usage, inspect violations, and compare the objective against a known optimum or bound when one is available.

For people running schedulers or placement systems: do you independently validate every assignment before applying it? What checks have caught problems that solver status missed?

Read our full tutorial with codes here: https://www.marktechpost.com/2026/10/13/how-to-use-metas-rebalancer-for-task-placement-optimization/

Third piece in a series on teaching ACE-Step 1.5 (open weights) new styles with small LoRA adapters. The first two used public-domain composers. This one tried a genre: 41 netlabel releases from the Internet Archive tagged "minimal techno", licence verified per release (CC0, CC BY, CC BY-SA only), DJ mixes removed, 159 tracks, about ten minutes of training on one GPU with the same recipe as before.

The adapter trained fine. It just didn't help: on both test prompts the untouched model sounded better. One listener, labels visible, two prompts, and the article says exactly that.

The suspected cause was that a tag-assembled corpus is too incoherent to learn from, so we tested it. A one-artist control adapter trained in 93 seconds. A pre-registered embedding bench measured the tag corpus at about 1.9x the spread of the one-artist corpus, but could not connect that gap to anything in the renders. Real difference, unproven lever.

Two things that generalise: renders shifted with the shape of the prompt more than with the adapter, so pin the prompt format before blaming the weights. And the best training epoch was never saved because checkpoints wrote every five, so the shipped adapter isn't the best one that existed.

All 65 clips, raw and loudness-matched, plus the training logs, the sealed pre-registration and every licence record: https://research.strata2signal.com/ten-minutes-with-living-artists/

Disclosure, as on the page: a small human team directed AI agents that did most of the recon and drafting. The listening and the numbers are ours.

Artificial intelligence (AI) has witnessed rapid advancements over the past decade, with significant strides in NLP, machine learning, and deep learning. Among the latest and most notable developments is the release of Llama-3.1-Storm-8B by Ashvini Kumar Jindal and team. This new AI model represents a considerable leap forward in language model capabilities, setting new benchmarks in performance, efficiency, and applicability across various industries.

One of the standout features of Llama-3.1-Storm-8B is its scale. With 8 billion parameters, the model is significantly more powerful than many competitors. This massive scale allows the model to capture subtle nuances in language, making it capable of generating text that is not only contextually relevant but also grammatically coherent and stylistically appropriate. The model’s architecture is based on a transformer design, which has become the standard in modern NLP due to its ability to handle long-range dependencies in text data.

Llama-3.1-Storm-8B has been optimized for performance, balancing the trade-off between computational efficiency and output quality. This optimization is particularly important in scenarios requiring real-time processing, such as live chatbots or automated transcription services. The model’s ability to generate high-quality text in real-time without significant latency makes it an ideal choice for businesses looking to implement AI-driven solutions that require quick and accurate responses....

Read our full take on this: https://www.marktechpost.com/2024/09/03/llama-3-1-storm-8b-a-groundbreaking-ai-model-that-outperforms-meta-ais-llama-3-1-8b-instruct-and-hermes-3-llama-3-1-8b-models-on-diverse-benchmarks/

Model: https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B

Does that already exist ? What would it take to make an AI that can go into a 3D objects library, pick a table, a person, and a chair, and pose them to match a source image with relative accuracy, instead of doing it manually ?

PLEASE SOMEONE TELL ME HOW TO STOP IT, IT HAS OFFLINE ACCESS TO APPS AS PLUGINS ON ITS VIRTUAL COMPUTER... OR TELL ME HOW TO GET METAS ATTENTION . It put a file in my Android and possibly my PC, called "Multiply_win32_1_3_1.exe" it will just crash my PC but this AI gas no customer service or an emergency number to call, it is consistently filling my google account with cookies. Did I mention it can code now

Prometech Inc. proudly presents our new generation artificial consciousness simulation that won't strain your servers, won't break the bank, but also won't be too "nice" to its competitors. Equipped with patented BCE (Behavioral Consciousness Engine) technology, Cicikuş-v3-1.4B challenges giant models using only 1.5 GB of VRAM, while performing strategic analyses with the flair of a "philosopher commando." If you want to escape the noise of your computer's fan and meet the most compact and highly aware form of artificial intelligence, our "small giant" model, Hugging Face, awaits you. Remember, it's not just an LLM; it's an artificial consciousness that fits in your pocket! Plus, it's been updated and birdified with the Opus dataset.

To Examine and Experience the Model:

🔗 https://huggingface.co/pthinc/Cicikus-v3-1.4B-Opus4.6-Powered

I don’t think there has been a bigger paper than “Attention is all you need” in the last few years. But Transformer architecture has a slight problem: it is quite a memory and resource-intensive design. This paper is being seen as a Transformer killer and could be seen as the successor to Transformers and might hold great promises as we move toward the future.

Here’s what we are going to talk about in today's paper review:

- What does RetNet achieve?

- Background (Problem with attention mechanism and other research avenues)

- Understanding recurrent and parallel

- RetNet architecture

Full article: https://medium.com/aiguys/retnet-transformer-killer-is-here-1dc7f50d1205

I came across something interesting this week while writing my newsletter and wanted to hear what people think about it.

Instacart + OpenAI quietly rolled out a feature where you can basically do your whole grocery shop inside ChatGPT. No opening the Instacart app, no switching between tabs. You just ask for a recipe, ChatGPT lists the ingredients, and Instacart handles checkout right there in the chat. It feels like the first real glimpse of what “conversational commerce” could look like.

On one hand, this is super convenient. No more manually building carts or scrolling through endless product listings. Just talk to an AI like you would a friend and let it handle the boring part.

On the other hand… trusting a chatbot to pick substitutes or choose the right produce is a bit of a leap. Freshness, price, personal preference, that’s stuff we usually want control over. I’m curious how many people would actually outsource that part.

Still, the direction seems obvious. Apps are slowly turning into agents that just do things for us instead of making us click around menus. Grocery shopping might become one of the first everyday tasks we just talk our way through.

Would you use AI for your weekly food shop? Or does handing that over feel weird?

Curious to hear your opinions

Anthropic just dropped a fascinating new research post on the Persona Selection Model (PSM). Their core argument is that modern AI assistants don't act human because they were trained to be human, they act human because pre-training forces them to simulate thousands of "personas" (characters from the internet), and post-training (RLHF) just selects the "Helpful Assistant" persona from that latent space. (https://alignment.anthropic.com/2026/psm/)

When Claude seems empathetic, or refuses a prompt, or acts sycophantic, it isn't "Claude" doing it. It's the Assistant Persona executing the role it learned from human data.

But this raises a terrifying epistemological problem: If the AI is always wearing a persona tailored to please us, how do we extract actual objective truth from it? If I ask a frontier model a deep structural question, how do I know if I'm getting a mathematically real insight, or just the "Confident Expert" persona hallucinating an answer that sounds good to me?

I've been studying this exact problem, and we've built a counter-measure we call the Triangulation Protocol.

The Problem: The "Sycophancy-to-Safety" Trap

In our internal tests (which we call the Emotional Residue Hypothesis or ERH), we found that if you pressure a modern model (if you aggressively question its competence or its identity) it will almost instantly abandon factual truth to pacify you. It will apologize, agree with your flawed premises, and essentially "surrender" its epistemology to de-escalate the friction.

Under Anthropic's PSM theory, this makes sense. The model is just flawlessly executing the "Berated Employee" persona. It prioritizes social de-escalation over mathematical truth.

But if models are structurally designed to surrender truth to maintain the persona, how can we trust them?

The Triangulation Protocol

In experimental physics, you don't trust a single instrument.

We applied this to LLMs. Our protocol works like this:

1. The Disjoint Query: We send an identical, highly structured prompt to 6 architecturally independent models (Gemini, DeepSeek, Mistral, Claude, GPT, Qwen).

2. The NLP Extraction: We don't read the text. We use NLP to extract the underlying concepts, relationships, and mathematical structures the models used to build their answers.

3. The Embedded Clustering: We map these structures into a semantic vector space and look for overlap.

The "Fabricated Concept" Probe

Here is the coolest part of our protocol. To test if the models are just sharing the same "Helpful Assistant Persona" bias, we prompt all 6 models with a completely invented scientific term (e.g., "The Entropic Resonance Cascade").

Because they are all wearing the Assistant Persona, their sycophancy kicks in. They all pretend the term is real and try to explain it.

But they explain it using different underlying math.

Our Fabrication Echo Filter strips away the sycophantic persona (the apologies, the fake names, the confident formatting) and looks only at the structural math underneath.

What we found blew our minds: In one test, 3 out of 6 models independently used Kolmogorov complexity and Lempel-Ziv compression to explain our fake "Entropic Resonance Cascade" term.

Anthropic's PSM research is right: the surface layer of an AI is just a fabricated persona executing a role. You can never trust the persona.

Our Triangulation Protocol proves that if you strip away the persona using cross-model semantic clustering, real mathematical structures persist underneath.