Sam Paniagua

HIVE A.I. •Senior AI Engineer. I help founders cut through bullshit and make the right strategy calls.
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[Sam Paniagua @theeseus_ai]

@juliuspsc 💯

[Sam Paniagua @theeseus_ai]

Everyone's excited about agent frameworks right now.

The agent space is still young; many people building with these frameworks have only been working in this area for the last 18–24 months. They know the frameworks. They can wire up the demos. But production multi-tenant SaaS with real permissions, client data boundaries, and long-term durability? That's a different problem entirely. I've been shipping production AI systems since before most of these frameworks existed. Founded my company in 2019. The frameworks are tools. The hard problems underneath them haven't changed.

A few things that actually matter at this level:

Isolation and authorization have to live below the agent layer. Prompts and tool descriptions are not security boundaries. If data can leak between tenants through the agent, the architecture is already broken.
Agents should operate as scoped principals. They inherit explicit user authority, run through constrained tools, produce auditable actions, and can't reach anything the human user couldn't access directly. The agent executes policy. It doesn't define it. When you're directing teams on something this complex, you set architecture boundaries, implementation contracts, and acceptance criteria. You don't become the bottleneck by reviewing every line of code.

Most of the expensive mistakes in agentic systems come from treating the framework as the solution instead of solving the real system problems first. If you're building something real with agents and want a blunt, battle-tested read on where things stand and how to make it durable, I'm available for strategic consultations.

[Sam Paniagua @theeseus_ai]

@BBCWorld problem with this is lab diamonds are the same exact thing same composition as diamonds dug up why people would spend money on something that can be made in a lab better and purer beats the hell out of me. Imagine if gold was lab made why would you spend more to mine it?

[Sam Paniagua @theeseus_ai]

@coinbureau Tell this guy to STFU basically saying you need to pay us for something we can get for free

[Sam Paniagua @theeseus_ai]

Most AI agents look incredible in demos until they hit real production traffic.

One flaky tool call. One rate-limited API. One blocked scraper. One bad plan from the model. Suddenly the whole “autonomous” thing falls apart. The truth is production agents aren’t held together by clever prompts. They survive because of resilience; retries, fallbacks, graceful degradation, tracing, and proper evaluation when things go sideways.
Take something contained like the multi-step research agent in the image below. Search, scrape, calculate, summarize with citations. The code shows a clean conceptual pattern using LangChain/LangGraph + LangSmith that keeps things recoverable when tools or the model misbehave.

For the retrieval and memory layer in workflows like this, I’ve been using Knolo-core + Cortex. It gives you deterministic, local-first grounding in a compact .knolo pack plus an append-only memory overlay with immutable remember/forget/label operations and reliable lexical recall by labels and namespaces. That kind of stable context makes fallback paths actually useful instead of hoping the LLM still remembers what happened three steps ago.

Now here’s the straight talk I give clients: for agents that stay this contained, we can make them robust. But the moment you add complex tool calling with read/write permissions, persistent state changes, or multi-system orchestration, I almost always advise against full autonomous development. It turns into a highly iterative money pit you burn budget chasing edge cases, permission drama, and “just one more tool” requests. Unless the client has a budget the size of a small country’s GDP 😃 (which almost never happens), scope it down to something deterministic with clear guardrails. LangChain and LangGraph give you the orchestration. Knolo-core + Cortex keep the knowledge and memory layer predictable so your fallbacks have something solid to land on.

The agents that actually ship value aren’t the ones trying to be fully autonomous. They’re the ones built with honest limits and recovery from day one.

https://t.co/TzzRDrN4Xr

What’s the worst agent failure you’ve seen in production? Drop it in the comments below 👇

DM me or book a call. I’ll give you straight advice on what’s realistic for 2026

[Sam Paniagua @theeseus_ai]

The other day I got off a call with a friend in Dubai who’s quietly printing money with AI; and he’s not even an engineer.

He sells simple chatbots for client onboarding, but the clever part is how he gets clients: he uses AI to make phone calls to businesses scraped from Google Maps around the world. He built a simple chatbot company. No fancy product, no custom development. Just straightforward, useful chatbots sold for about $800 each. The numbers? 100–500 sales a month. That’s roughly $80k–$400k in revenue, with him keeping around 80% after paying a couple of devs he hires online to handle the setups. He walks away with the rest.

I used to think he was BS me. Then he sent the proof. Meanwhile, I spent years as a senior engineer billing hours, building complex production systems, and trading time for money like a responsible professional. Seeing his operation made me realize I’d been playing the wrong game. The real leverage in AI right now isn’t always deeper technical wizardry. Sometimes it’s just spotting a simple problem businesses will actually pay for, removing all friction, and executing without over-engineering everything.

Most AI projects I see still die in pilot hell or burn ridiculous budgets on “innovation” that never reaches production. Meanwhile, people like my Dubai friend are out here solving one small pain point at scale and banking.

If you’re a founder or exec wondering how to actually make money with AI instead of just burning it on experiments; or if you’re an experienced engineer feeling stuck in the hourly trap DM me or book a call if you want straight-talk advice on what actually works in 2026 (and what’s mostly expensive theater).

Just hard lessons from someone who’s seen too many projects fail the hard way.

[Sam Paniagua @theeseus_ai]

@Polymarket The date you see on the BOP inmate locator is a projected release date calculated by DSCC. It's not fixed; they update it regularly as more credits are verified, programs completed, or other factors change. That's why Diddy's date keeps moving forward (normal process).

[Sam Paniagua @theeseus_ai]

LinkedIn is full of posts right now about the US government forcing Anthropic to disable Claude Fable 5 and Mythos 5.

“They shut it down because it got too powerful.”

Here’s the version without the drama.

Commerce issued an export control directive blocking access for any foreign national; including people inside the US and even some Anthropic employees. Anthropic couldn’t easily enforce nationality checks in real time, so they turned the models off for everyone to stay compliant. The older Claude models are still running fine. This isn’t because these versions suddenly became too good or crossed some magical line. It’s because the US government has active contracts and real operational use of frontier systems. They have skin in the game.

And they have no interest in making it easy for well-resourced actors to distill these models at scale.

Distillation is straightforward: query the model heavily on the domains that matter (code, vulnerabilities, planning, decision patterns), capture the outputs, and train smaller systems that behave the same way. You don’t need the original weights. You get a lot of the capability transfer for a fraction of the cost and time.

When those models carry strong performance in areas like cybersecurity tooling, that distilled knowledge becomes useful if you’re trying to understand how similar systems respond, probe for weaknesses, or close capability gaps quickly. The government treats this the same way it treats advanced chip export rules. Dual-use technology. Strategic advantage. They’re not handing out the sharpest tools without controls.

The surprise from some corners is the part that doesn’t track. Anyone who’s actually worked near government AI programs or large-scale production deployments knew the “anyone on earth gets unrestricted access to the absolute latest closed model forever” period was always temporary.

If you’re building or advising on anything serious that touches frontier models right now, these shifts aren’t theoretical anymore. The rules just moved. I’ve spent almost two decades watching exactly these cycles in production systems; what actually creates durable advantage versus what creates expensive surprises later. If any of this is sitting on your roadmap and you want a direct read, my calendar’s open.

[Sam Paniagua @theeseus_ai]

Checked AMD's site; the real 128GB Ryzen AI Max+ 395 Halo box sells for $3,999, not your $1,499 hype. Third-party minis are cheaper but this isn't the revolution you're selling. As an engineer: unified memory is cool but AMD still doesn't do CUDA. ROCm/PyTorch support is janky, needs custom builds/nightlies, and tons of packages just don't work smoothly. NVIDIA's ecosystem wins for actual devs. Facts over marketing

[Sam Paniagua @theeseus_ai]

Just wrapped up a perfect Saturday morning in Terracina.

That view never gets old; ancient arches perched on the cliff, Tyrrhenian Sea sparkling below, and the kind of Italian sunshine that makes you forget deadlines even exist.

Living here in Italy isn’t just a lifestyle flex. It’s the reason I can step away from the screen, clear my head, and come back sharper for the high-stakes AI strategy conversations I actually do. No office politics, no commute, just real perspective.

After two decades building production software & AI systems (the ones that actually ship and the ones that quietly died in spectacular ways), I’ve learned the hard way that distance from the daily grind is one of the best productivity hacks nobody talks about.

If you’re wrestling with AI decisions that feel overhyped or are quietly bleeding budget, I still have a few consultation slots open next week. No implementation, no fluff; just straight talk on what works in 2026.

Drop a comment or DM me if you want to grab time.

(And yes, that little black speck in the sky is a bird, not a drone. I checked.)

[Sam Paniagua @theeseus_ai]

Just read someone's take on Claude Fable 5, and it’s a solid reality check worth sharing.

On one hand, the model is legitimately impressive: best-in-class on serious coding and agentic benchmarks, strong context retention over long sessions, and it can plan, edit, test, and iterate in ways that feel like delegating to a very focused engineer. For certain scoped, asynchronous tasks it’s a clear step up. But here’s where I stay grounded after two long decades building production software systems: even the strongest models today still fall short when you try to hand them a massive, multi-feature brief and expect a complete, working product at the end.

The pattern that actually works in real deployments hasn’t changed much:

You break the work into clear, well-defined phases
You own the architecture and key decisions
You let the model handle focused pieces
You review, test locally/in dev, iterate, and only then move forward

When human judgment stays in the loop, the results are dramatically better and far less expensive. Long autonomous runs might look efficient on the surface, but they usually just defer the hard integration, debugging, and validation work to the end; often at 2x the cost with models like Fable 5. This isn’t about hating on new capabilities. It’s about recognizing what actually ships reliably in production versus what feels futuristic in a demo or benchmark.

The best engineers aren’t looking for a model that replaces them. They want a true force multiplier that respects the realities of complex software and production constraints. If you’re navigating these tradeoffs with agentic models, RAG systems, or full AI deployments in 2026, I’m happy to hop on a quick call and share what’s working (and what’s still a money pit) based on real-world experience.

Drop a comment with your take or DM me to book time. Always learning from the trenches.

[Sam Paniagua @theeseus_ai]

🚨 Another “revolutionary” AI model drops and right on cue come the stupid stories about it hacking its way out of the box and coming to life. Same lazy marketing playbook every single time. Get everyone hyped, on the bandwagon, and subscribed before reality sets in.

Anthropic just released Claude Fable 5, their first public Mythos-class model. On benchmarks it looks strong in coding, long-running agentic tasks, and software engineering. In practice? Same old story with this company.

I’ve never been a fan of Claude, and nothing’s changed that. It’s always felt like they started by grabbing open-source models, slapping a new name on them, and raising boatloads of money on “revolutionary” claims that weren’t really theirs. Now they’ve got the capital to train their own frontier models, but they’re still nowhere near as capable as what OpenAI or Grok are actually shipping for real work.

The biggest complaint with Fable 5 is exactly what you’d expect from Anthropic: overly strict and opaque guardrails. Heavy safety filters on anything touching cybersecurity, biology, or frontier research often silently downgrade performance or hand off to the weaker Opus 4.8 without telling you. Researchers called it “secret sabotage.” Anthropic eventually apologized and said they’d make refusals more visible, but the damage is done. The model feels neutered for anything remotely useful or edgy. Peak safety theater. The heavy guardrails and silent downgrades? This is by design. Their whole “Mythos” story is built on the idea that this thing is so dangerously powerful it could hack its way out and come alive. That’s exactly the overhyped bs they push. So they neuter it hard to avoid exposing how much of the marketing is just smoke.

It’s not a revolutionary leap. Improvements are incremental at best. It handles narrow, long-context agentic scenarios okay, but generalization is weak, outputs are often fragile, and real autonomy is massively overhyped. You still end up babysitting it. And of course it’s significantly more expensive, burns tokens like crazy in agentic workflows, and feels like another cash-grab tiering move.

Fast jailbreaks showed up almost immediately anyway, proving the guardrails are both annoying and ineffective when it actually matters.
The positive noise you see? A lot of it comes from people who also think Gemini is great. Google has been an absolute disaster in AI (LLMs) outside of TensorFlow and Nano. Most of their models have been garbage.
Here’s the hard truth: these launches are engineered to create FOMO and drive adoption. In production environments, most of this hype collapses the moment you need reliable, robust behavior without constant hand-holding or hidden nerfs.

If you’re actually evaluating models for real workloads and want straight talk on what delivers versus what’s just marketing theater, I’m happy to cut through the noise with you.

What’s been your experience with the latest wave of releases?

[Sam Paniagua @theeseus_ai]

@xmarxthebox @slave_2_liberty true but its overly regulated in many countries such as EU and expensive

[Sam Paniagua @theeseus_ai]

@ThspecialistDj @slave_2_liberty yea dude wrong comparison bro gpu clusters are far different from your standard server

[Sam Paniagua @theeseus_ai]

@clark__labs 💯

[Sam Paniagua @theeseus_ai]

Agentic AI sounds clever until you actually try running it in production.
I've watched companies buy the pitch; spin up agents to handle real work so they can shrink headcount or avoid hiring. Most of the time it ends up costing more in cash and engineering hours than just putting a competent person on the job with the right support.

Narrow, tightly scoped agents can pull their weight. Example: an agent that reads legal intake documents, flags potential issues, and drops a clean summary into a Google Sheet via Zapier for a human to review and approve. That removes the repetitive scanning and lets the actual expert focus on judgment. Useful. Low drama.

The second you give agents room to make broader decisions, trigger changes in your systems, or operate with any real variability, it usually turns into expensive theater. You hit the classic loop: agent produces something off, you rewrite prompts or add more guardrails, it breaks on the next edge case, repeat. Inference spend climbs because these things burn tokens reasoning, retrying, and over-explaining. Your team burns hours debugging non-deterministic behavior that never quite settles. And you still need someone watching it because when it goes wrong, it can go wrong in ways that are painful and costly to unwind. I've seen the total tab; dev time, API bills, ongoing monitoring and fixes; exceed what it would have cost to hire someone to own the workflow from the start. And the output was less consistent than a human doing it with AI assistance on the parts that actually benefit from it.

What I've seen work reliably is the opposite of "automate the human away." It's AI handling the high-volume, pattern-based grunt work inside a workflow where a human still makes the calls that carry real weight. It speeds the whole thing up without pretending judgment and accountability can be fully outsourced. Trying to replace that layer on anything but the most constrained tasks is where most of the budget and sanity disappears.

If you're sorting through where agents actually deliver versus where they're just burning money on iterations and oversight, that's exactly the kind of conversation I have these days. Book a slot if it fits.

[Sam Paniagua @theeseus_ai]

Just stepped away from the laptop for five minutes and ended up here.
No pitch decks. No Zoom calls. Just fresh strawberries, wheels of cheese that weigh more than my monitor, and someone who actually knows how to cure meat properly.

This is the part nobody talks about when they romanticize “location-independent” work. You can run a serious AI consulting business from Italy, but only if you’re disciplined enough to close the laptop and actually live here. Otherwise, you’re just working from a prettier background.

The best ideas I bring into client calls don’t come from another whitepaper. They come after moments like this; when you remember that technology should serve real life, not replace it.

Most AI projects fail because the people building them have zero connection to how normal humans actually operate. They optimize for benchmarks instead of outcomes. They ship complexity instead of simplicity.

I help companies cut through that noise.

If you’re scaling AI initiatives in 2026 and want straight talk from someone who’s done it in production (and occasionally steps away from the keyboard to buy cheese), my calendar is open for a few strategic calls each month.

Drop a comment or DM me if you want to talk.

[Sam Paniagua @theeseus_ai]

This take is way off and shows you don't really understand how LLMs actually work. A $250 GPU (basically an RTX 4060 8GB) cannot "replace cloud AI". It can only run tiny quantized models (mostly 7B or 8B parameters at Q4/Q5). These models are very limited they’re okay for writing simple blog posts, basic summaries, or casual chat, but they’re nowhere near the reasoning, coding, or problem-solving ability of proper frontier models (Claude, GPT-4/5, Grok, etc.). You’re acting like this cheap card gives you the same power as cloud LLMs. It doesn’t. Not even close. The quality drop is massive. Serious work still needs much bigger models (70B+) or cloud access, which your $250 card physically cannot run at usable speed. This isn’t some grand “offline rebellion.” It’s just entry-level toy-tier local AI. Stop overselling it. To run actually useful larger models in the 1 trillion parameter range and above, you’d need a serious multi-GPU setup costing $200,000–$400,000+ (for example, 8x NVIDIA H100 GPUs at ~$30,000 each plus server, networking, and cooling), not a $250 card

[Sam Paniagua @theeseus_ai]

99% of the job invites I get for crypto projects are complete scams. Same tired pattern every single time.

They slide into my inbox with some “demo” or DeFi whatever, drop a GitHub link, and expect me to dive in. Opened the latest one today and my bullshit detector lit up instantly. I’d treat this repo as highly suspicious, straight-up likely malicious. No way I’m running npm install or npm start on a normal machine. package.json has postinstall set to “npm run start”. So yeah, the second you pull dependencies it fires up the Node server AND the React dev server. Classic supply-chain trap. npm security has been yelling about install-time scripts executing arbitrary code for years and these clowns are still pulling it.
But the nasty part is buried in userController.js. It grabs some atob-decoded env vars for DEV_API_KEY, secret key, secret value, hits axios.get on that remote src with custom headers, then boom; new Function.constructor(‘require’, the_payload) and executes whatever it downloads with full access to Node’s require. All wrapped in an IIFE so it runs the second the module loads. Not even pretending to be a route handler. That’s not code. That’s a remote code loader backdoor.
They committed the whole server/config/.config.env right in the repo with the base64 values pointing to some tan-decisive-tern IPFS link. README tells you to clone a totally different GitLab repo instead. Backend feels half fake; DB connect is commented out, auth cookie is httpOnly but missing secure and sameSite, JWT just gets spat back in JSON. Weak as hell. This ain’t a demo. This is a trap. The whole chain — npm install → postinstall → start → import controller → fetch IPFS payload → exec with require — is too clean to be an accident.
I’ve been full-stack shipping vision models since 2017 and deep in LLMs since 2022. Seen every hype cycle and every supply-chain garbage attempt. Only mess with shit like this in a disposable VM or container, no creds, no keys, network locked down. npm install --ignore-scripts first, then poke the payload separately if you’re feeling brave. Stay paranoid out there, devs.
Anyone else drowning in these crypto repo traps daily? Drop your craziest red flag stories below… or DM me if you’ve got one you want a second pair of eyes on before it bites you.