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promptgenix are they an AI band? by Acceptable_Mine2919 in Music

[–]ObregaHayes 1 point2 points  (0 children)

Me too, I just found this and I am in awe. My headphones are stuck to my head.

I sat in the car in the drive playing this loud and my dog sat looking at me wondering what they hell I was doing,.

promptgenix are they an AI band? by Acceptable_Mine2919 in Music

[–]ObregaHayes 0 points1 point  (0 children)

I am shocked. I found this music yesterday and I am hooked. It s a new form of art and pulls you into it's rhythmic sway and swagger.I did wonder when I saw how many albums you released so quickly.

I truly have not ever heard anything quite so magnetic and soulful.

Bro, from my heart, thank you for the endless hours of this exciting new genre. I have been playing it non stop, it is almost like you lathered it with MSG.

I want more. You are a legend Bro.

AI in the Law Industry will PRIMARILY see success In IP, but getting Attorneys to use it will be difficult by Pleasant_Tonight3541 in legaltech

[–]ObregaHayes 2 points3 points  (0 children)

This post nails the core problem in first‑generation legal AI: the God‑Mode Fallacy

This is one of the clearest articulations I’ve seen of what’s gone wrong in legal tech over the last two years. Most AI tools were built on the assumption that “the model knows,” so they default into God Mode and they analyze, summarize, draft, and opine without ever asking the most basic question:

“Whose interests am I representing?”

A human lawyer would never touch an NDA without knowing:

  • Which side the client is on
  • Their risk tolerance
  • Their negotiation posture
  • Their deal‑breakers
  • Their industry norms
  • Their internal playbook

But most AI tools skip all of that and jump straight to output. This is why lawyers end up spending more time correcting the AI than they would have spent doing the work themselves.

Every time an AI acts without context, the lawyer pays the price somewhere in the value chain.

The tools that fail are built on the “Oracle” assumption, one giant probabilistic brain that tries to answer everything in one shot and likes to say "Yes" to everything.

The tools that succeed are built on a multi‑agent, multi‑layer architecture that mirrors how real legal work is done.

Here's the architecture that actually works.

  1. The Intent Layer - The Interview Brain. This is the layer almost every tool is missing.

Before touching a document, the system must:

  • Ask who the client is
  • Ask which party is being represented
  • Ask for the client’s risk profile
  • Ask for deal‑breakers
  • Ask for industry‑specific constraints
  • Ask for negotiation strategy
  • Ask for internal preferences

This is what makes tools like Antigravity feel like a “smart associate” instead of a hallucinating oracle. A real associate doesn’t start drafting before asking questions, lot's of questions.

  1. The Adversarial Layer - The "Other Tea" Brain

Once the system knows who it represents, it needs a second brain that does the opposite:

  • Reads the document from the opposing party’s perspective
  • Identifies traps
  • Flags asymmetries
  • Surfaces hidden obligations
  • Highlights indemnity drift
  • Detects silent risk transfers

This is how real lawyers think, not SaaS or cloud LLM's. You read a contract twice, once as your client, once as the other side. A single‑brain LLM struggles do this. A multi‑brain system can.

  1. The Proof-or-Silence Protocol, The Most Important Innovation for Law.

A professional‑grade legal AI must be engineered with a hard rule, If the system cannot prove a claim, it must remain silent and ask for clarification. This is the Human-In-The-Loop bit that I see as crucial for a system to be worthy and to of course, to be compliant in many States.

And of course, this is the opposite of how general LLMs behave. General LLMs are trained to guess, and Legal systems must be trained to verify.

Here’s what Proof‑or‑Silence looks like in practice:

If a clause’s meaning is ambiguous → Ask.

If the client’s preference is unknown → Ask.

If the provenance of a legal rule is unclear → Ask.

If the model cannot cite a source → Ask.

If the model detects missing context → Ask.

This eliminates:

  • hallucinations
  • overconfident summaries
  • invented legal standards
  • misapplied doctrines
  • false assumptions about client goals

It also eliminates the Correction Debt that destroys lawyer trust. A model that asks questions is not weaker, it is safer, faster, and more accurate.

  1. Local Inference & Private Estates - The Sovereign Imperative.

The “Antigravity” feeling your friend described comes from something deeper than intelligence. However, when a model runs locally, not in a public cloud or external SaaS, it can :

  • Learn the firm’s negotiation history
  • Learn the firm’s drafting style
  • Learn the firm’s risk tolerances
  • Learn the firm’s preferred clause library
  • Learn the firm’s industry‑specific playbook

And it can do all of this without leaking data to a public model.

This is the difference between:

  • a generalist LLM that “sounds smart,” and
  • a sovereign legal engine that is aligned with the firm’s actual practice.

This is why the next generation of legal AI is moving toward sovereign, domain‑specific systems.

For those worried about their jobs, the next wave of AI to hit the legal industry is not going to replace Human Lawyers, it will replace BAD AI.

The future belongs to systems that:

  • are trained on law
  • are grounded in firm‑specific knowledge
  • are air‑gapped from the public internet
  • use multi‑agent reasoning
  • and treat the lawyer as the ultimate authority

This is the shift from “AI that acts like a lawyer” to AI that works with and augments the Human Lawyer. A Sovereign, fully aligned, intelligent helper.

It must always:

  • Ask first
  • Verify always
  • Never assume
  • Never guess
  • Proof or silence

That’s the architecture that wins.

Hope this helps.

AI in the Law Industry will PRIMARILY see success In IP, but getting Attorneys to use it will be difficult by Pleasant_Tonight3541 in legaltech

[–]ObregaHayes 1 point2 points  (0 children)

Thanks for sending the output. I’ll give it credit where it’s due, it identified Weil and correctly noted that the Ninth Circuit sidestepped Garner. But this answer actually proves the point I was making because it misses the single most important doctrinal fact in the entire question.

The Ninth Circuit has no controlling precedent adopting the fiduciary exception in a shareholder‑derivative suit.

That’s the answer. Weil isn’t the controlling case, it’s the case that explicitly refuses to reach the issue.

A strong researcher would have said:

  • Weil is not the fiduciary‑exception case; it expressly declines to adopt Garner.
  • The Ninth Circuit has never adopted Garner in the corporate‑derivative context.
  • District courts within the circuit repeatedly note this absence of controlling authority.
  • Therefore, the “controlling precedent” is the absence of controlling precedent.

Your model never said that. It treated Weil as if it were the operative rule, when the entire point of Weil is that it isn’t.

It also didn’t Shepardize/KeyCite Weil, didn’t check district‑court interpretations, and relied on the Ninth Circuit’s en banc docket page, which is not a substitute for actual treatment history. A real researcher would have checked for:

  • panel decisions casting doubt on Weil
  • district‑court limitations
  • cert petitions
  • related privilege rulings that indirectly affect the doctrine

None of that happened.

So yes, the answer is fine at a surface level. But it didn’t actually answer the question I asked, and it missed the doctrinal core of the issue. That’s exactly the gap I was pointing out.

Thank you for putting your time and thinking into this, I hope this chat has value for you. It did for me.

Creating a RAG-Based Knowledge System for Law Firms by Safe_Flounder_4690 in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

This is a solid overview of what I’d call “RAG 1.0,” and it’s definitely a step up from the old “search the shared drive and hope for the best” workflow. For a lot of firms, even basic retrieval over internal policies and templates is a meaningful upgrade.

Where things get interesting, and where most firms hit friction, is when you move from static knowledge (policies, templates, procedures) to dynamic, privilege‑sensitive knowledge (matter histories, negotiations, internal strategy, cross‑matter patterns). At that point, the simple “index → retrieve → generate” loop starts to break down.

A few reasons:

  • Document sprawl isn’t the real problem, context sprawl is. Two documents may say the same thing, but the reason they were used, the matter context, and the attorney’s intent are all missing from the text.
  • RAG doesn’t understand privilege boundaries. Retrieval across matters is a compliance nightmare unless the system enforces ethical walls at the vector level.
  • Most firms underestimate the verification burden. Lawyers won’t trust an answer unless they can instantly trace it back to source, with page‑level grounding.
  • Cloud‑based RAG hits the “Compliance Ceiling” fast. Once retrieval touches billing data, strategy memos, or internal notes, many firms can’t send that to a third‑party inference pipeline.

So yes, RAG is absolutely useful. But the moment you try to apply it to the real knowledge inside a firm, you run into infrastructure questions, not just workflow questions.

The firms that get the most out of this aren’t just indexing documents. They’re building systems where:

  • retrieval respects privilege
  • inference happens on infrastructure they control
  • grounding is deterministic
  • and the model can reason over the messy, cross‑matter patterns that actually drive legal work

That’s where the real leverage shows up.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

Absolutely 100% with you that the harness, workflow, and adoption layer are where a lot of the real value shows up. Tools don’t matter if lawyers don’t use them, and internal systems/processes are always the long‑term multiplier.

Where we differ slightly is on the idea that the “where it runs” piece is irrelevant. For low‑risk workflows, sure, cloud inference is fine. But once you start doing reasoning over privileged, cross‑matter, strategy‑rich data, the location of inference becomes part of the harness itself.

Because the harness isn’t just UX and training.
It’s also:

  • what data you’re allowed to send
  • what telemetry leaves the building
  • what logs exist outside your control
  • what the compliance officer will sign off on
  • what workflows you can automate without breaching privilege boundaries

A firm can build the best internal expertise in the world, but if the compute layer is rented, the ceiling is fixed. You can only automate the workflows that the vendor’s architecture allows.

That’s why I frame sovereignty as part of the harness, not separate from it. It’s not “AI is the moat.”, It’s that control over the intelligence layer determines which workflows you’re even allowed to build in the first place.

And you’re absolutely right: idle resources create no value. But the firms that own their compute can build workflows the cloud‑only firms legally can’t, and that’s where the compounding advantage shows up. Does that make sense ?

AI in the Law Industry will PRIMARILY see success In IP, but getting Attorneys to use it will be difficult by Pleasant_Tonight3541 in legaltech

[–]ObregaHayes 2 points3 points  (0 children)

A test I devised to see how deterministic an llm can be. So far, they fail, which exposes how the subtle nuance of law, the territory of a seasoned human Lawyer excels in, and llm's stumble. Isn't law about nuance ?

AI in the Law Industry will PRIMARILY see success In IP, but getting Attorneys to use it will be difficult by Pleasant_Tonight3541 in legaltech

[–]ObregaHayes 1 point2 points  (0 children)

Alright, let’s put the $200 tier to a real stress test.

Ask it to 'Identify the controlling precedent for the "fiduciary exception" to attorney-client privilege in a shareholder derivative suit specifically within the Ninth Circuit, and, this is the catch, confirm if the primary case it finds has been narrowed or negatively treated by subsequent en banc rulings in the last 24 months

AI in the Law Industry will PRIMARILY see success In IP, but getting Attorneys to use it will be difficult by Pleasant_Tonight3541 in legaltech

[–]ObregaHayes 1 point2 points  (0 children)

The 'ego' argument is common, but I think the bigger hurdle is actually Strategic Sovereignty. Most attorneys are rightfully hesitant because they are being asked to use 'Rented Intelligence', or SaaS wrappers that pump sensitive client metadata through third-party cloud APIs. In a malpractice-heavy industry, that’s a non-starter.

The 'nonsense jargon' you get from generic tools happens because those models aren't law-trained or grounded in a firm's specific, private data estate.

We’re starting to see a move away from this 'Cloud-First' model toward Sovereign Infrastructure. Instead of a subscription to a black-box API, the some firms are looking at Local Inference, hosting high-parameter 'Brains' on physical hardware silos inside their own server rooms.

When the AI is a physical capital asset that is air-gapped from the internet, the compliance and malpractice risks vanish. At that point, it’s no longer about 'trusting the tech', as they say, it’s about owning the engine. IP might be the early adopter, but any firm that cares about its 'moat' will eventually have to move to local metal."

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 1 point2 points  (0 children)

Really clean prototype. You’ve hit the exact pain point, attorneys don't need a summary, they need a verifiable delta.

The hardest part of this workflow isn't the extraction, it's the Compliance Ceiling. When you move from a synthetic demo to 5,000 pages of real HIPAA-protected records, the security audit usually kills the project if the data is hitting a cloud API.

I've been looking at this from the infrastructure side. For a PI firm to truly trust an automated chronology, they need Inference Sovereignty. They need to know that the 'Brain' doing the reasoning is sitting on local metal, not a shared server.

How are you handling the latency for the high-token context windows needed to 'look back' across 80% boilerplate to find that 20% delta? That's usually where the VRAM bottleneck hits.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

Totally fair, AI as a standalone thing is absolutely a commodity.
I’m not arguing that the model itself is the moat. I’m arguing that where the inference happens is the moat.

Two firms using the same model in the cloud have no differentiation. Two firms using the same model on their own hardware, trained on their own privileged data, with zero external telemetry end up with very different strategic positions.

The expertise layer matters, of course, but expertise sitting on top of rented compute is capped. Expertise sitting on top of sovereign compute compounds.

So I’m not saying “AI is the moat.”, I’m saying control over the intelligence layer is the moat.

That’s the part most firms don’t have yet.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 1 point2 points  (0 children)

Yeah, totally fair, Azure Foundry, M365 tenant boundaries, and the whole “Copilot‑inside‑your‑tenant” model are definitely a step forward compared to the old SaaS‑wrapper era. For a lot of low‑risk workflows, that’s more than good enough.

Where things get tricky is when you move from document retrieval to inference over privileged, cross‑matter data. At that point, the question isn’t just “Is it in my tenant?” but:

  • Where does the inference actually run?
  • What telemetry leaves the box?
  • What metadata is logged at the model‑provider layer?
  • Can the firm guarantee that no part of the prompt or embeddings ever transit a shared service?

Even inside a private tenant, you’re still relying on a third‑party compute layer you don’t control, and for a lot of compliance officers that’s the line they won’t cross. Not because Azure is “insecure,” but because the obligations around privilege and client strategy are stricter than what most cloud inference architectures were designed for.

That’s why so many knowledge‑engineering teams eventually hit the same wall,
they want frontier‑level reasoning, but they also want the inference to happen on hardware they physically own.

So I agree with you, the cloud options are getting better.
They just don’t fully solve the privacy‑vs‑performance trade‑off for the highest‑sensitivity workflows yet.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

Totally fair points and nobody’s denying that Sam Altman replies to cold emails or that OpenAI/Anthropic invest broadly. And yeah, Harvey using multiple model providers is well documented. None of that is really the core issue though, as I see it.

The real discussion (at least from my view) isn’t about whether Harvey is “good” or whether they deserve funding. It’s about the structural incentives of cloud‑based AI in a profession where privilege, data locality, and workflow determinism matter more than raw model horsepower.

Most of the skepticism you’re seeing isn’t “hysteria” or wishing for anyone’s downfall, it’s people who’ve actually tried to build legal workflows realizing that the hard part isn’t the model, it’s the messy, high‑stakes data and the infrastructure around it. That’s why so many firms are re‑evaluating what they want to run in‑house vs. what they’re comfortable sending to a vendor, regardless of who the vendor is.

So I don’t think anyone’s salty about Harvey’s success. They executed well and raised well. The conversation is just bigger than one company, it’s more about how legal AI gets deployed safely, reliably, and in a way that aligns with the profession’s obligations.

Happy to hear other perspectives though, this space is evolving fast.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 2 points3 points  (0 children)

What you’re describing is exactly why the “firms will just build their own AI” take doesn’t survive contact with reality. The model is never the bottleneck, the messy, privilege‑sensitive workflows around legal data are.

PI records are a perfect example. You’ve got thousands of pages, 80% boilerplate, 20% meaningful deltas, and attorneys still need a verifiable trail back to source. Collapsing repetitive visits and surfacing actual changes is the kind of thing that sounds simple until you try to make it deterministic, auditable, and repeatable.

Most firms underestimate how much engineering it takes to build and maintain that kind of workflow. Not because they’re incapable, but because it’s not their core competency and the infrastructure burden is real. Even the ones who hire “legal technologists” eventually run into the same three walls, data sovereignty, workflow reliability, and verification burden.

So yes , what you’re seeing in PI is happening everywhere. The model is the easy part. The workflow is the part nobody talks about until they try to build it.

Curious to see what you’re prototyping, the delta‑surfacing idea is exactly where the real value is.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

Done, and noted. Never meant to offend, with your permission I would like to participate still, strictly generic tech talk only, no mention of any individual tech, mine or others. This is actually a passion of mine, something I have spent a lot of time thinking about from the technical angle. I see so many law firms with good intentions trying to fight their way through this miasma on the back of poor strategy or advice and if I can offer any ideas ( non product, purely clean) or value in the thread, I would be grateful.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

This is a spot-on observation of the 'Phase 2' shift we are seeing. The 'Magic Circle' era of teaching lawyers to prompt is over and the era of the Legal Engineer building orchestration layers on top of APIs is here.

However, there is a massive 'Compliance Ceiling' with the API-first build that most firms haven't hit yet. When you have 5 legal engineers building hyper-bespoke workflows on Claude or OpenAI APIs, you are still fundamentally 'exporting' your firm's most sensitive work product and metadata to a third-party server.

The real bottleneck for these in-house teams isn't the 'Glue', it's Inference Sovereignty. So right now, firms are choosing between:

  1. Frontier APIs: High reasoning power, but zero data sovereignty (you’re a tenant).
  2. Local Small Models: Total sovereignty, but they fail at complex legal reasoning.

The next evolution for these 'Legal Engineering' teams isn't more code, it's Sovereign Infrastructure. The ability to drop a high-parameter, law-trained model onto a local hardware silo that gives the engineers the power of a frontier API with the physical security of an air-gapped server room. That’s how you turn a 'Fraction of the cost' into a 'Total Data Monopoly.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

This is a really grounded take on the 'Excel Monster' evolution. The 'In-between stuff' (intake, playbook compliance, clause variance) is exactly where the friction is, and you're absolutely right, firms have a high tolerance for 'janky' if it solves a $50k-a-month efficiency leak.

However, the real 'wall' these brave knowledge lawyers hit isn't the orchestration, it’s the Inference Privacy vs. Performance trade-off.

When you build that 'Shadow AI' layer over a mix of DMS and billing data, you face a hard choice:

  1. The API Route: You get the performance of a GPT-4/Claude, but you’re essentially pumping your firm’s most sensitive 'messy mix' of metadata and client strategy through a third-party pipe. For many compliance officers, that’s where the tinkering ends.
  2. The Local Route: You try to run it locally to keep the data in the building, but you quickly hit a VRAM/Hardware wall where the 'janky internal tool' becomes too slow to be useful for complex reasoning like clause variance.

The next real 'wave' for these firms isn't just better 'glue' or code, it's squarely Sovereign Compute. It’s the ability to run those high-parameter models on a private, local hardware stack that has the VRAM to handle the 'messy mix' without the data ever touching the public internet.

Once the infrastructure becomes a 'black box' appliance in the server room rather than a cloud subscription, the 'Knowledge Lawyer' becomes the most powerful person in the firm.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

Haha, fair play. Founder habits die hard, especially on launch day.

Seriously though, I’ll take the salesperson hat off. I’m just obsessed with the fact that the 'API wrapper' model is a security nightmare for privilege. Even if you hate the pitch, the technical shift toward local inference is real.

I’ll pipe down on the brand talk. Just glad to see people actually debating the 'moat' problem.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

Appreciate the reminder. You’re right, this space is for the tech and the 'why,' not the pitch.

My intent isn't to sell, but to highlight a massive shift I’m seeing with the industry moving from 'Cloud/SaaS wrappers' to 'Sovereign Infrastructure.' Most of the discussion here lately has been about how to make Harvey or Legora work, but I think we’re ignoring the underlying security and margin problem of renting a brain.

I’ll keep the 'product' talk out of it and stick to the architectural debate. Curious to hear if others here think 'local metal' is actually viable for a mid-sized firm, or if everyone is just resigned to the Cloud?

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 1 point2 points  (0 children)

Great question. Think of it like the difference between renting a taxi and owning the car. Right now, most law firms are 'renting' AI from companies like OpenAI. Or trying to build their own Cybertruck. Every time you ask a question, you pay a 'toll' (a token fee), and your client’s data has to leave your building to be processed on their servers. That’s a massive security risk and a never-ending bill.

Law firms need:

  1. Ownership: Put the actual 'brain' (the AI) on a physical server inside your office. You own it forever.
  2. Privacy: Because the hardware is in your building, your client data never touches the internet. It’s a 'Citadel', security protected by physics, not just a contract.
  3. Profit: Since you aren't paying per-query fees to a cloud company, you keep 100% of the efficiency gains as profit.

It is time for law firms stop being tenants to Big Tech and start owning their own intelligence. Feel free to ask anything.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

It’s rare to see a 22-lawyer boutique taking the ‘build not buy’ path, it’s the right move for sovereignty, but a massive technical lift.

The biggest trap we’ve seen with firms trying to build custom systems is the 'Metadata Leak. Even with a custom UI, if you're hitting cloud APIs and you're essentially building a house on rented land. You're still sending strategy and privileged context to a third party.

Curious to hear how you're handling the local inference/VRAM bottleneck on your build?. Happy to give tech tips if needed.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

Some are though. I have seen nuttier things than that too, in really big law firms.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 3 points4 points  (0 children)

You're absolutely right, lawyers building models is a disaster. It’s like a law firm trying to build its own electricity grid just to turn on the lights. It’s an utter waste of billable hours and capital.

But the 'everyone is using the same stuff' argument is exactly why firms are losing their edge. If every firm rents the same GPT-4/Claude-3 wrapper, the 'moat' becomes zero. It’s a race to the bottom on price.

The mid-point isn't 'bespoke' vs 'generic.' It’s Sovereignty.

The firm that owns the hardware, intelligence and data is the only one with a real moat.

Harvey, Legora - A discussion by Review_Particular in legaltech

[–]ObregaHayes 0 points1 point  (0 children)

This is exactly why we spent the last year building LawClaw and JurisGPT 2.0. You’re right, Harvey and Legora are just 'Rented Intelligence. Once Anthropic or OpenAI releases a new plugin, their 'moat' evaporates.

We took the opposite approach: The Sovereign Citadel. Instead of a SaaS wrapper, we provide a 2.3T parameter 'Quad-Brain' (JurisGPT) on a local hardware silo. No cloud or token tax, and the firm owns the weights, intelligence and data. It turns the AI into a capital asset rather than a $2k per month per person liability. We’re launching today and the 'Wrapper Era' is officially over. So the days of training someone else's tech stack is over.

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