Diffco

Its not models, its always harness. Our CEO Vadim Peskov on the Goodfirms podcast: why AI coding tools speed up typing but not teams, and what delivers 5-30X on real systems.

Diffco has been named an Industry Leader on the Goodfirms Leaders Matrix for Software Development - Embedded Software Development. We are rated 5.0/5 #IndustryLeader #SoftwareDevelopment

Diffco named a Top 10 IoT Development Company for Q3 2026 by 50Pros!

Our CEO Vadim Peskov on the NoRobots Podcast, on what clients are actually buying when AI writes the code.

So your team is 100x now with AI. But 100x at what?" That's roughly where my conversation with Pale Blue Nexus started, and it's the question nobody asks. We've genuinely cut client project costs by 4x. On smaller new builds we're at essentially 100% AI-written code. But the multiplier framing hides the part that actually matters: the limiting factor was never typing speed. It was architecture and product decisions — and those are still made by people. We also talked about why better models won't improve your output much (the harness matters more), why open source wins on private data, and the pricing crisis heading for the mid-market. 42 minutes, no hype: https://lnkd.in/gvXw8VPy

Why you still need developers in 2026 — and why "vibe coding everything" is a trap A vibe-coded product usually lasts 3–9 months before the foundations crack. Not because the tools are bad. They're remarkable — a clickable prototype in an afternoon, an internal dashboard before lunch. We use them ourselves. The trap is treating "great for prototypes" as "great for products." What breaks is boringly predictable. Schemas that are roughly right and exactly wrong. Auth that looks plausible and isn't. No tests, no observability, and nobody holding the system in their head. Feature velocity halves, then halves again, and someone eventually rebuilds under time pressure with paying customers watching. Here's the part that surprises founders: one developer brought in during the first 60 days usually prevents the crack entirely. That cost is a fraction of the rebuild. Developers in 2026 type less code than they did in 2024. They make more decisions — and each decision now flows through AI-amplified output, so it compounds faster in both directions. So don't stop vibe coding. Just don't vibe code forever. https://lnkd.in/gT54FR5v

AI-native vs AI-assist development. Two completely different conversations are happening under the label "AI in software development," and leaders keep budgeting for one while asking for the other. AI-assist is engineers shipping faster with Copilot, Cursor, Claude Code. The product behaves identically — the user can't tell AI was involved. AI-native is software whose runtime behavior depends on models. Probabilistic, drifting, expensive per request. The user absolutely can tell. Different skills. Different failure modes. Different hires. For AI-assist you don't hire "AI engineers" — you hire strong product engineers and invest in review rigor, type safety, and test discipline so the AI is a multiplier instead of a liability. For AI-native you hire people who have operated LLM systems in production. Shipped a RAG system with an eval set. Have war stories about prompt regressions. Rarer and more expensive. Plan for it. The costly mistake: treating AI-assist productivity as evidence your team can ship AI-native. Don't let "we're using AI" be one line on the roadmap. Split it, fund each separately, measure each on its own terms. https://lnkd.in/gDntfQjR

AI in fintech: building secure agentic workflows for regulated environments Fintech AI runs 12–18 months behind the broader market. Not because the engineering is harder — because the failure cost is higher and the path through compliance is longer. You can compress that lag without inheriting the risk that created it. A few positions we hold firmly: Models propose, deterministic services execute. The model never holds the keys to a money-moving API. It emits a structured action object; a service re-validates every field and applies policy checks before anything happens. Even after the user confirms. Policy gates are code, not prompts. "Amount over X requires dual approval" is a unit-tested function with explicit failure behavior. The model can make mistakes. The gate cannot. Audit logging at the model layer. Every prompt, output, model version, tool call, retry — with identity, timestamp, and policy decision. Write-once. Searchable by compliance without engineering in the loop. And the one we don't move on: no agent takes an action against money or identity without a human confirming, regardless of how confident it looks. The best predictor of success isn't architecture. It's whether compliance was in the room in week one. https://lnkd.in/gcPmasDr #Fintech

Self-hosted vs API-based LLMs: the 2026 cost-and-control tradeoff "Should we self-host to save money?" lands in our inbox a few times a quarter. Usually from a CFO staring at a bill. The honest answer is that most teams who run the numbers properly stay on the API — and the ones who don't run the numbers end up paying more for worse output. What gets left out of the self-hosted estimate: GPU fleets sized for peak sit idle most of the time, so real utilization lands at 30–60% and effective cost is 1.5–3x the headline rate. Then inference engineering — batching, KV-cache, speculative decoding — which is free in API pricing and 3–5x throughput if you skip it. Then the engineering time, the eval ownership, the reliability scar tissue you haven't earned yet. Break-even usually arrives at sustained hundreds of millions of tokens a day. Not before. But cost is sometimes the wrong frame entirely. Data residency, sovereign cloud, latency floors, big LoRA portfolios — there the comparison isn't self-hosted vs API, it's self-hosted vs no product. The strongest architectures we see are hybrid: frontier API for the hard 20%, small self-hosted model for the easy 80%, cache in front, router deciding. Pick a posture, not a religion. https://lnkd.in/gDev7myh

Multi-agent system architecture: a teardown of how we built a customer support agent We shipped a customer support agent handling a five-figure number of tickets a week. 84% resolution in month one, 88% by month three. We did not start by deciding to build a multi-agent system. We started with a problem and an eval set, and got there over four iterations because single-agent kept hitting walls. Iteration 1: one model, one 4,000-token prompt, all five ticket categories. Instructions bled across categories. Middling accuracy, premium rates for what should have been cheap classification. Iteration 2: router plus one solver. Bleed-through stopped, complex categories still failed. Iteration 3: router plus specialists. 58% → 81% in a week. Then we noticed 7% of tickets touch two categories and the router was throwing half the answer away. Iteration 4, shipped: triage classifier, parallel specialists, orchestrator that stitches structured outputs, reviewer, and a confirmation step. Deliberately unfashionable choices — agents communicate in JSON, not English. No autonomous actions on money or identity. Strict tool boundaries per agent. If you're reaching for multi-agent because it's interesting rather than because single-agent failed you, you're going to overspend. https://lnkd.in/gMm7Ek7J