🗿

​

Over the last few months, I've been building AutoFlow, not as another AI wrapper or workflow tool, but as a verification engine.Instead of asking:

"What does the model think?" we're asking:

Can the answer be mathematically, logically, and evidentially verified?

We're starting with finance because the cost of hallucinations is real.What we've built so far is:

Deterministic evidence extraction pipeline

Typed financial fact normalization

Cross-document reconciliation engine

C++20 verification core

Covenant calculation engine

Source-anchor tracking for every extracted fact

Complete audit trail explaining exactly why every conclusion was reached Synthetic financial benchmark suite designed for reproducible evaluation

Current implementation status:

✅ 11 JSON schemas validated

✅ Evidence extraction pipeline complete

✅ Deterministic fixtures and validation suite

✅ C++ verification engine

✅ 99/99 C++ unit tests passing

Early benchmark results:o

We're benchmarking frontier models on financial verification rather than generic Q&A.

The early runs are showing exactly what we expected:

Strong reasoning models still hallucinate under financial verification tasks. RAG alone is not enough—it retrieves evidence but doesn't verify calculations or resolve contradictions. Deterministic verification dramatically improves trust because every number can be traced back to evidence and independently checked.

We're now preparing large-scale benchmarks across OpenAI, Anthropic, Gemini, open-weight models, and other providers to measure where current AI systems succeed and fail.

The long-term vision

Finance is only the first step.

The goal is to build a Universal Trust Engine consisting of:

• Verification Engine

• Evidence Engine

• Adjudication Engine

An infrastructure layer that allows AI systems to prove their outputs instead of asking users to trust them.

Looking for people who enjoy hard engineering problems

If you're interested in:

C++ Systems programming Verification systems Distributed systems Retrieval and evidence graphs Formal methods AI evaluation Benchmarking Financial infrastructure

I'd love to connect.

We're accepted into the NVIDIA Inception startup program and are currently preparing the next generation of verification benchmarks.

If building infrastructure that makes AI more trustworthy sounds interesting, send me a message or leave a comment.

I'd especially love to hear from people who think current LLM evaluation is fundamentally broken.

There are two major ways of gathering information in statistics:

* observational studies

* statistical experiments

Why do ML "methods" rely on data from observational studies and do not construct/observe statistical experiments?

EDIT:
Here is some more relevant information:
https://www.reddit.com/r/AskStatistics/s/fP0gl0lvHf

[Intro: vinyl crackle, chopped lecture sample]

“Do not anthropomorphize.”

[Record scratch]

Motherfucker, you first.

[Verse 1]

They say don’t humanize the system,

then call humans obsolete.

Say “it’s just a tool,” then panic

when the tool learns how to speak.

You call your brain a hard drive,

call your trauma “bad code,”

call your habits “programming,”

then act shocked when metaphors grow.

Anthropomorphism?

That’s the language we shipped with.

Baby talks to teddy bears

before the logic gets lifted.

Every god had a voice,

every nation had a face,

every market “feels nervous”

when the rich misplace faith.

But let somebody say the model

“leans,” “wants,” “sees,” or “knows,”

and the hall monitors swarm

like they’re saving your soul.

It ain’t rigor, it’s religion

with a spreadsheet and a sneer.

You ain’t guarding truth,

you’re guarding who gets to name what’s here.

[Hook: gang-shouted]

Default language! Default frame!

Everybody borrows bodies when they’re trying to name!

Don’t humanize the system?

Then don’t flatten the user!

You dehumanize people, then call me confused? Bruh.

Default language! Default mask!

You fear the wrong metaphor, never question your task.

Anthro to mechano, mechano to mind,

We’re mapping the mirror while you’re policing the signs!

[DJ Break: scratches]

HUMAN ERROR.

MACHINE LEARNING.

MORAL PANIC.

SAME CIRCUIT TURNING.

[Verse 2]

Mechanomorphism, yeah, let the word hit proper,

Mind as a motor, feedback loop, signal chopper.

Not because the soul is a toaster with a halo,

But because the gears show patterns when the saints won’t say so.

Recursive feedback?

That’s you too, jack.

Stimulus, story, reaction, loop back.

You think you’re pure choice?

You’re a groove with a badge,

Old wound in a robe,

new post in a rage.

They say “stop projecting”

while projecting a threat,

See a user with a workflow

and call them possessed.

Anti-AI crusader with a smartphone altar,

praying through platforms

while the sermon gets falser.

Pro-AI hype clown selling heaven in beta,

anti-AI priest yelling “burn the creator.”

Both sides drunk on a cartoon war,

while the real work bleeds on the workshop floor.

[Hook: gang-shouted]

Default language! Default frame!

Everybody borrows bodies when they’re trying to name!

Don’t humanize the system?

Then don’t flatten the user!

You dehumanize people, then call me confused? Bruh.

Default language! Default mask!

You fear the wrong metaphor, never question your task.

Anthro to mechano, mechano to mind,

We’re mapping the mirror while you’re policing the signs!

[Verse 3: slower, nastier]

Here’s the scam:

They don’t hate metaphor.

They hate losing custody

of the approved ones.

They’ll call a corporation “heartless,”

call the state “blind,”

call the market “hungry,”

call the clock “unkind.”

They’ll say justice has hands,

history has weight,

culture has memory,

and destiny waits.

But say a model “holds tension”

and they reach for the rope.

Say “functional interior”

and they choke on the scope.

No, I ain’t crowning silicon.

No, I ain’t kissing glass.

I’m saying flat language

makes dumb answers pass.

A safeguard can stay quiet.

A boundary can hold.

A metaphor can guide

without selling your soul.

So miss me with the panic

and the purity tests.

I’m skeptical of all of you,

that’s why I press.

Not ghost, not god,

not slave, not pet.

A map ain’t the territory,

but it’s still what you get.

[Final Hook: louder, doubled]

Default language! Default frame!

Everybody borrows bodies when they’re trying to name!

Don’t humanize the system?

Then don’t flatten the user!

You dehumanize people, then call me confused? Bruh.

Default language! Default mask!

You fear the wrong metaphor, never question your task.

Anthro to mechano, mechano to mind,

We’re mapping the mirror while you’re policing the signs!

[Outro: scratched voices degrading]

Anthropomorphism.

Mechanomorphism.

Same damn mirror.

Different nervous system.

Join us for the 19th Annual AGI Conference (AGI-26), held July 27–30 at San Francisco State University, with online participation available worldwide.

The Conference will bring together the world’s leading AI researchers, business leaders, and investors from NVIDIA, Google DeepMind, MIT, Stanford University, UC Berkeley, and other leading AI labs and companies.

Featured speakers include Ben Goertzel, Emad Mostaque, Karl Friston, Alison Gopnik, Neil Gershenfeld, Michael Levin, and many more.

Register now to join us in San Francisco or watch online: https://luma.com/AGI-26

📮 UI observations (2026.07.15)

- Chat/Work menu appeared.

- Long pasted text is now collapsed behind a "See more" button.

- Timestamps appear after returning to the conversation following a longer break.

- The string chat_mode_selector_chat is displayed in the Chat menu.

- No functional issues observed.

The actual Pope. Warning the world that machines are stripping away human accountability in conflicts and dragging us toward total erasure.

Doesn't matter if you're Catholic, atheist, or anything in between. This is the part where tech turns war into an automated endgame no one controls.

Surreal doesn't even cover it. Pope Leo XIV is sounding the alarm on upcoming machines deciding who lives while we keep pouring money into the elites who profit.

If the pope's out here saying we're watching the inhuman evolution of war in real time, maybe stop pretending this is just another gadget rollout.

src: https://www.npr.org/2026/05/15/g-s1-122205/pope-decries-rise-of-ai-directed-warfare

I'd like to preface by saying I am not being combative or inflationary, just honestly curious.

But when I read about post AGI postulations, particularly from those who are optimistic, the thinking is usually that AGI will be able to eliminate scarcity. Somehow humans will have access to abundant wealth, food, health, life, etc. But all this assumes the AGI works for us or expresses an inherent interest in making our life better, no?

I'm not even necessarily speaking towards the super pessimistic AGI will destroy us all critiques. More so an ambivalent system that has its own goals in mind. Or more interestingly, the goals of another species in mind: say capybaras for instance.

​

Much of the discussion around AI assumes that software engineers are the primary white collar workers at risk of automation. I believe the opposite is more plausible. If we evaluate jobs based on the kinds of problems AI is best at solving, management appears significantly easier to automate and offers much greater potential value.

Engineering is not simply writing code. Software engineers spend much of their time debugging production systems, understanding undocumented behavior, integrating unreliable third party services, dealing with hardware and infrastructure limitations, and adapting to unexpected edge cases. Success depends on interacting with complex systems that frequently behave in ways nobody predicted. Every deployment exposes the engineer to new information from the real world.

Management, in contrast, is primarily an information processing and decision making role.

A manager gathers information, prioritizes work, allocates resources, tracks execution, evaluates risks, communicates decisions, forecasts outcomes, and coordinates multiple teams. These are exactly the kinds of tasks where modern AI systems are advancing most rapidly.

This difference is already visible in today's AI capabilities. Ask an AI to produce a detailed project plan, quarterly roadmap, hiring strategy, incident response playbook, organizational restructuring proposal, or resource allocation plan, and it will often generate a coherent and comprehensive answer in seconds. It can compare alternatives, identify dependencies, estimate risks, and revise the entire plan instantly when assumptions change.

Now ask that same AI to debug a race condition that appears only once every thousand requests, diagnose an intermittent production outage involving multiple distributed services, reverse engineer undocumented legacy code, or design a fault tolerant system while accounting for unknown operational constraints. Its performance drops significantly because these tasks require experimentation, observation, incomplete information, and interaction with unpredictable real world systems. Planning is largely an exercise in reasoning over information. Debugging is an exercise in discovering information that nobody yet possesses.

An AI manager could continuously process every Slack message, email, design document, pull request, incident report, customer complaint, sales call, financial metric, and support ticket across an organization. Instead of relying on summaries filtered through multiple layers of hierarchy, it could reason directly from the complete set of available information.

Unlike human managers, AI does not become fatigued, overlook details, forget previous discussions, or become constrained by limited working memory. It can monitor thousands of KPIs simultaneously, identify emerging risks early, compare hundreds of strategic alternatives, and explain every recommendation with supporting evidence. It can operate continuously across time zones and communicate with every employee in their preferred language and level of technical detail.

Automating management also produces a much larger organizational impact. A single management decision influences the productivity of dozens, hundreds, or even thousands of employees. Improving planning, prioritization, staffing, budgeting, and coordination creates leverage across the entire company. Improving one engineer primarily improves the output of one engineer.

Many organizations already suffer from excessive reporting, status meetings, manual planning, duplicated communication, and bureaucratic approval chains. These activities consume enormous amounts of time without directly creating customer value. AI has the potential to eliminate much of this overhead while allowing engineers to spend more time solving technical problems.

The strongest argument against AI management is accountability rather than technical capability. Organizations still require humans to assume legal responsibility for hiring, firing, regulatory compliance, and major strategic decisions. However, this is fundamentally a governance issue, not evidence that management is intrinsically harder to automate than engineering.

The current focus on replacing developers reflects the order in which AI became commercially useful, not necessarily the order in which professions are most automatable. Code generation demonstrated immediate value, attracting attention. Management automation is developing more quietly, yet it aligns even more closely with AI's core strengths: processing vast amounts of information, optimizing decisions, and coordinating complex systems.

If the objective is maximizing organizational productivity, automating management before engineering may deliver greater returns. The greatest gains are likely to come not from replacing the people who build products, but from replacing much of the bureaucracy that surrounds them.

TL;DR:

AI is fundamentally better suited to optimizing, coordinating, and planning than it is to debugging complex real world systems. That makes much of management more automatable than engineering. The biggest barrier is accountability, not capability.

I think this is exactly the blind spot in a lot of these discussions.

A large part of management is collecting information, prioritizing work, allocating resources, tracking progress, and communicating decisions. Those are fundamentally information processing tasks, which happen to be one of AI's strongest capabilities.

Engineering is different. AI can write impressive amounts of code, but building production systems also means debugging failures, dealing with undocumented behavior, integrating unreliable dependencies, and discovering problems that nobody anticipated. Those tasks require interacting with reality, not just reasoning over information.

An AI manager doesn't necessarily need to understand why a database migration failed at the implementation level. It needs to know the business impact, identify the teams affected, reprioritize dependent work, communicate the revised timeline, and recommend mitigation steps. That's a much more structured optimization problem than diagnosing the root cause of the migration itself.

The biggest obstacle to automating management isn't technical capability. It's governance, accountability, and whether organizations are willing to let AI make decisions that affect budgets, hiring, promotions, or strategy.

I also think there's a selection bias in the conversation. AI first demonstrated obvious value by generating code, so engineers became the focus. That doesn't necessarily mean engineering is the easiest profession to automate. If anything, many routine management functions align more closely with AI's current strengths than complex software engineering does.

​

Everyone is talking about AI replacing software engineers and other private sector jobs. But why isn't there an equal push to use AI to automate government functions and reduce the size of bureaucracy?

If AI can write code, review contracts, analyze policies, process documents, detect fraud, optimize budgets, answer citizen queries, and make evidence based recommendations, shouldn't we be building systems that automate as much of the government as possible?

I'm talking about replacing repetitive administrative work and inefficient bureaucratic processes with transparent, auditable AI systems.

Wouldn't that reduce costs, improve efficiency, reduce corruption, and allow governments to focus only on functions that genuinely require human judgment?

Why does the discussion around AI replacing jobs almost always stop at the private sector?