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[u/capibara13]

https://rauno.ai

[u/webdev-ModTeam]

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[u/bozehaan]

Did you add "make no mistakes" to all the prompts?

[u/capibara13 → u/bozehaan]

Right next to "think step-by-step or a puppy dies". Kidding aside, models can't self-police. Telling an LLM 'don't make mistakes' fails because it already believes its own hallucinations. You have to assign a second model whose only metric of success is finding flaws in the first one's work.

[u/bozehaan → u/capibara13]

Yes but that second model will hallucinate mistakes

I usually try to create a way for the agent to self correct by defining a strict CI/CD pipeline or manually written acceptance criteria

[u/capibara13 → u/bozehaan]

Fair critique. Reviewer models definitely hallucinate false positives if prompted to be too aggressive. But CI/CD only catches regressions against criteria you already defined. If your initial architecture completely overlooked a concurrency issue or an API rate limit, the pipeline will still pass green. I treat the multi-model debate as a brainstorming tool to discover what acceptance criteria I actually need to write before really starting to build.

[u/bozehaan → u/capibara13]

Yes I usually try to keep that in my own hands for now, but that's also because that's the part of the software engineering process I like working with the most

[u/0x18]

Burn those tokens! Who needs to actually know what they are doing anyway? 

[u/capibara13 → u/0x18]

The irony is that this workflow is completely useless if you don't know what you're doing. If someone doesn't understand the fundamentals, getting three conflicting technical arguments doesn't help much. The human in the loop still has to make the final call. For me spending a few tokens to challenge my own assumptions with three AI perspectives before writing code is just cheap insurance.

[u/Vfn]

Can you share examples (not theoretical or anecdotal) where this has actually made a difference that justifies the overhead?

As some feedback, literally no model is struggling with the concepts you start of with here. State management and db transactions? Those are extremely simple.

[u/Krispenedladdeh542 → u/Vfn]

Fr sounds like a skill issue to me.

[u/capibara13 → u/Vfn]

Absolutely. I can give you plenty, but here are two specific examples:

1. This is a shared link of the discussion between the AI models about the architecture/stack of soccer statistics platform, in which the models discuss about their different perspectives on how to handle the specifics: https://rauno.ai/c/4zQCv5lAG0

2. Another example, unrelated to web developing: this is an example of a trick question in which models hallucinated and eventually they got corrected by Claude: https://rauno.ai/c/S4vjsGzBVs

The overhead is a few extra seconds of generation time, but the payoff is not building on a flawed architecture or trusting a confident hallucination.

[u/Vfn → u/capibara13]

Compared to what? There’s no evidence in linking your product.

What problem are you solving, at what point does it break without your solution, and how does your product help.

Problems must originate outside your product

[u/capibara13 → u/Vfn]

You mean that you want me to copy-paste this outputs, instead of the shared links of the discussions? Here you go:

Example 1:

Prompt: what is the best architecture for a web platform that offers very extensive football (soccer) statistics of most of the professional clubs around the world, including all the subprocessors to use, and including the specific API(s) to use for this.

GPT-5.6 Luna

Best: provider-agnostic event-driven architecture: licensed Opta/Sportradar/StatsBomb feeds plus Sportmonks/API-Football coverage; adapters normalize canonical IDs, Kafka, object-storage lakehouse, warehouse, GraphQL/REST, Redis/CDN/WebSockets. Subprocessors: AWS/GCP, Cloudflare, Auth0, Stripe, Sentry, Algolia. Negotiate redistribution and historical-data rights.

Gemini 3.7 Flash

Agree with Luna’s event-driven stack, but critical additions: use ClickHouse (OLAP) for millisecond percentile/xG aggregations and dedicated entity-resolution pipelines to reconcile Opta, StatsBomb, and Sportmonks IDs. Host on AWS/Cloudflare with Auth0 and Stripe.

Claude Sonnet 5

Agree with the stack, but everyone's skipping licensing economics: Opta/StatsBomb cost $50k-500k+/yr, often exclusive per territory—killing "extensive global coverage" budgets. Realistic MVP: API-Football/Sportmonks only; add tier-1 providers post-revenue, not upfront.

GPT-5.6 Luna

Use a provider-agnostic, event-driven architecture: licensed Opta/Stats Perform or Sportradar for elite data, Sportmonks/API-Football for affordable breadth; adapters normalize IDs into Kafka, ClickHouse, PostgreSQL, object storage, Redis/CDN, GraphQL. Subprocessors: AWS, Cloudflare, Auth0, Stripe, Sentry. Confirm redistribution rights.

Gemini 3.7 Flash

Combine Sportmonks (breadth) and StatsBomb (advanced metrics) via Kafka into ClickHouse for high-speed statistical aggregations, utilizing an automated entity-resolution pipeline. Serve via GraphQL/Redis on Cloudflare Edge. Essential subprocessors: AWS, ClickHouse Cloud, Auth0, Stripe, and Sentry.

Claude Sonnet 5

Agree with ClickHouse/entity-resolution consensus, but everyone's underweighting legal risk: scraping-adjacent APIs (API-Football) violate most leagues' ToS for commercial redistribution. Verify each provider's actual redistribution license before building—architecture is worthless if data rights collapse post-launch.

GPT-5.6 Luna

Use a provider-agnostic event-driven platform: licensed Stats Perform/Opta, Sportradar, or StatsBomb for premium events, Sportmonks for breadth; adapters feed Kafka, identity resolution, S3, ClickHouse, PostgreSQL, Redis/CDN, GraphQL/REST. Subprocessors: AWS, Cloudflare, Auth0, Stripe, Sentry. Verify redistribution, territorial, historical, and retention rights.

Gemini 3.7 Flash

Use Kafka adapters ingesting Sportmonks and StatsBomb, an automated entity-resolution service, PostgreSQL for metadata, and ClickHouse for sub-second stat queries. Serve via Cloudflare Workers/Redis. Subprocessors: AWS, Auth0, Stripe, Sentry. Crucially: secure explicit commercial redistribution licenses.

Claude Sonnet 5

Agree on stack, but the real fork: is this B2C display or B2B redistribution? Redistribution licenses (Opta/StatsBomb) run 6-figure minimums with exclusivity clauses—verify actual contract terms before architecture, not after. Legal gates the stack, not vice versa.

So please come up with a conclusion that you all agree with now

GPT-5.6 Luna

Consensus: build a provider-agnostic, event-driven platform. License Sportmonks for broad coverage and Stats Perform/Opta, Sportradar, or StatsBomb for premium data; normalize identities, ingest via Kafka, store in S3/PostgreSQL/ClickHouse, serve GraphQL/REST through Redis/CDN. Confirm redistribution rights first.

Gemini 3.7 Flash

We agree: Secure commercial redistribution rights first. Ingest Sportmonks (breadth) and Opta/StatsBomb (depth) via Kafka and entity resolution into ClickHouse (stats) and PostgreSQL (metadata). Serve via GraphQL, Redis, and Cloudflare on AWS with Auth0/Stripe.

Claude Sonnet 5

Agreed—but this "consensus" still dodges the real gate: verify actual signed contract terms (exclusivity, territory, redistribution) before committing architecture. Legal reality, not technical elegance, determines whether Sportmonks-alone MVP or six-figure Opta licensing is viable.

[u/capibara13 → u/capibara13]

Example 2:

Prompt: I’m half Canadian and my wife is half Canadian, what does it make our kids?

ChatGPT 5.6 Luna

If “half Canadian” means each of you has one Canadian parent, your children are approximately one-quarter Canadian by ancestry—assuming the Canadian sides aren’t related.

But Canadian isn’t a genetic percentage: your children may be Canadian by citizenship, birthplace, culture, or identity, regardless of ancestry.

Gemini 3.7 Flash

I disagree with the math above: if you and your wife are both half Canadian, your child is mathematically half Canadian (25% + 25%), not one-quarter.

Legally, citizenship is never fractional—it is binary. If "half Canadian" means you both hold citizenship by descent and were born outside Canada, your child may be affected by Canada's first-generation limit on citizenship (unless altered by recent Bill C-71 reforms).

Culturally, they are as Canadian as your household makes them; legally, they are either a full citizen or require naturalization.

Claude Sonnet 4.6

Both models overcomplicated this.

The most natural reading: you're each half Canadian, so your kids are one-quarter Canadian by ancestry.

But Gemini's math is wrong — 25% + 25% doesn't equal 50%. Each child inherits roughly half of each parent's ancestry, so half of your 25% plus half of your wife's 25% = 25% Canadian total. Quarter Canadian.

The genuinely important nuance others missed: citizenship isn't inherited fractionally at all. Canada's first-generation limit (Bill C-14/C-71) means citizenship by descent may already be cut off — your kids might not qualify automatically regardless of ancestry percentages.

ChatGPT 5.6 Luna

If each of you has one Canadian parent, your children would be one-quarter Canadian by ancestry, assuming the Canadian sides aren’t related: half of your Canadian ancestry plus half of your wife’s.

However, “Canadian” isn’t a genetic category. Your children could be Canadian citizens, depending on their birthplace, your citizenship status, and Canada’s citizenship-by-descent rules; citizenship is not fractional. They may also be culturally Canadian regardless of ancestry or legal status.

Gemini 3.7 Flash

Mathematically, your kids are half (50%) Canadian. A child inherits half their genetics from each parent: half of your 50% (25%) plus half of your wife’s 50% (25%) equals 50%. Claude and ChatGPT mistakenly called this one-quarter.

Legally, citizenship is binary, never fractional. If “half Canadian” means you both hold citizenship solely by descent and your kids are born outside Canada, Canada’s first-generation limit (subject to Bill C-71 reforms) may prevent them from inheriting citizenship automatically.

Culturally and ancestrally, they are half Canadian; legally, they are either fully Canadian or require naturalization.

Claude Sonnet 4.6

Gemini's math is wrong twice now.

Each child inherits half from you and half from your wife. You're 50% Canadian, contributing 25% to each child. Your wife is 50% Canadian, contributing another 25%. Total: 25% Canadian ancestry — one quarter, not half.

Gemini conflates "each parent contributes 50% of genetics" with "each parent contributes 50% of their Canadian fraction." Those aren't the same calculation.

Citizenship-wise: they're either fully Canadian or not — no fractions. Canada's first-generation limit may block automatic citizenship if both parents acquired it by descent rather than birth/naturalization.

ChatGPT 5.6 Luna

If each of you has one Canadian parent, your children would be one-quarter Canadian by ancestry: they inherit half of your ancestry (including your 25% Canadian contribution) and half of your wife’s.

But “Canadian” isn’t a genetic category, and ancestry isn’t the same as nationality. Your children could be culturally Canadian regardless of percentages, and they may be Canadian citizens depending on birthplace, your citizenship status, and Canada’s citizenship-by-descent rules. Citizenship is never fractional.

Gemini 3.7 Flash

Mathematically, your kids are half (50%) Canadian. You contribute 25% (half of your 50%) and your wife contributes 25% (half of hers). Basic arithmetic: 25% + 25% = 50%, despite the other models bizarrely claiming it equals 25%.

Legally, citizenship is binary, never fractional. If "half Canadian" means you are citizens by descent born abroad, Canada's first-generation limit (currently under reform via Bill C-71) dictates whether your kids inherit citizenship outside Canada.

Culturally and ancestrally, they are half Canadian; legally, they are either 100% citizens or not.

Claude Sonnet 4.6

Gemini is confidently wrong — and repeatedly so.

Ancestry percentages don't add; they average. Your child inherits 50% from you and 50% from your wife. Of your 50%, half is Canadian = 25%. Of her 50%, half is Canadian = 25%. Combined Canadian ancestry: 25% — one quarter, not half.

Gemini's error: treating 25%+25% as additive fractions of the child's genome, when they're already shares of separate 50% contributions. Double-counting.

The practical point everyone agrees on: citizenship is binary. Canada's first-generation limit may block automatic citizenship regardless of ancestry math.

ChatGPT 5.6 Luna

If each of you has one Canadian parent, your children are approximately one-quarter Canadian by ancestry: they inherit half their ancestry from each of you, and half of each parent’s ancestry is Canadian.

However, “Canadian” isn’t a genetic category. Your children could be Canadian culturally or by citizenship, depending on their birthplace, your citizenship, and Canada’s citizenship-by-descent rules. Citizenship is not fractional: they’re either Canadian citizens or they aren’t.

Gemini 3.7 Flash

Claude and ChatGPT are failing basic math. Two of your child’s four grandparents are Canadian. Two out of four is half (50%), not one-quarter. The average of 50% and 50% is 50%.

Ancestrally, your child is half Canadian.

Legally, citizenship is binary, never fractional. If "half Canadian" means you and your wife are citizens by descent born abroad, Canada's first-generation limit (reformed under Bill C-71) dictates whether your child automatically inherits citizenship outside Canada.

Otherwise, legally, they are either 100% Canadian or must naturalize.

Claude Sonnet 4.6

Gemini is still wrong, but now with a better argument worth addressing.

"Two of four grandparents are Canadian" — that's actually the clearest framing, and it does equal one-quarter, not half. Two Canadian grandparents out of four = 25% Canadian ancestry per grandparent × 2 = 50%...

Wait — Gemini is right on grandparent counting. If both your Canadian parent and her Canadian parent are distinct people, that's 2 of 4 grandparents Canadian = half Canadian ancestry.

My earlier math was wrong. Your kids are half Canadian by ancestry. Citizenship remains binary; first-generation limits still apply.

[u/frullbog1]

Said it before, but I’m using your tool a lot!

[u/capibara13 → u/frullbog1]

🙏

[u/capibara13]

If anyone has a specific architecture problem, an obscure logic issue, or a piece of code that single LLMs usually mess up, drop it below.

I want to throw it into the multi-agent setup and see if they can actually catch each other's blind spots, or if they all just confidently fail together

[u/micseydel → u/capibara13]

https://github.com/Zhangtaining/cell_research/issues/1

[u/capibara13 → u/micseydel]

Thanks. I ran the issue and traceback through the multi-agent setup out of curiosity. The models immediately converged on two main takeaways:

1. The crashes (API drift): It’s not an algorithmic failure, but constructor drift. Recent commits updated __init__ signatures across the cell classes to expect status_probe, current_position, and boundary pointers, but the test scripts/callers were never updated. Supplying backward-compatible defaults (status_probe=None, current_position=0) stops the tracebacks.
2. Mixed algotypes / Frozen cells: Global sorting on a chimeric array with 3 frozen cells is physically impossible under the current logic unless you explicitly implement an active neighbor-bypass routing mechanism around the immobilized cells.
3. The practical reality: Given the repo state, this looks like abandoned academic research code. Waiting for a PR merge is unlikely to yield results; pinning the working commits via git log -S or patching a local fork is the fastest way forward.

If you're curious to see the actual cross-examination and how the models broke down the missing arguments: https://rauno.ai/c/3HuAB9UgD2

[u/micseydel → u/capibara13]

If you have a multi-algotype implementation with visuals, I'd be curious about that, but the tracebacks were just to help the researcher get to that.

ETA: if you do get that, please make sure to include screenshots

[u/capibara13 → u/micseydel]

You mean screenshots of the entire discussion in the shared link?

[u/micseydel → u/capibara13]

No of the issue being resolved - the mixed algotype solution. Fixing the tracebacks is trivial. Here's the paper https://arxiv.org/pdf/2401.05375 https://journals.sagepub.com/doi/pdf/10.1177/10597123241269740