r/founders

spent the weekend building a lightweight domain warmer so i wouldn't burn my main inbox. it spins up disposable inboxes and sends real replies to boost reputation scores. open sourcing the script tonight for anyone who wants to test it.

OpenAI dropped GPT-5 this morning. SWE-bench jumped from 71 to 84 percent on first run. Tool use is now native rather than a separate API.

Many new founders feel scared to ask simple questions about legal or funding basics. This thread is a safe space to request plain English explanations for confusing terms. You do not need to know everything before you start building.

We migrated from GPT-4 to a distilled 7B model for our summarization endpoint last month. Cost-per-output dropped from $0.04 to $0.016 while maintaining 92 percent quality scores. Gross margin expanded from 55 percent to 78 percent without changing customer pricing.

Most founders ignore that inference costs often exceed LTV in early cohorts, a reality confirmed by Sequoia's $600B AI spend analysis. Benchmarks from Anyscale show that without aggressive caching, latency spikes destroy retention before product-market fit is proven. Build on verified margins, not demo-day hype.

Intercom released Fin Agent 2.0 with full ticket resolution capabilities, cutting average handle time by 40 percent in beta tests. GitHub Copilot Workspace now supports multi-repo context windows up to 1M tokens for complex refactoring tasks. Both tools offer immediate API access for early adopters.

We removed the free plan last month and forced a 14-day trial with credit card entry. Monthly recurring revenue jumped from $18k to $22k while support ticket volume dropped from 450 to 180 per week. The 400 users who left were costing us $0.08 per API call in infrastructure with zero conversion probability.

We burned $400 in a weekend because the model kept trying to fetch order details that didn't exist. Added a hard stop at three retries and forced a human handoff prompt when that limit hits. It cut token usage by 60 percent and actually improved CSAT scores.

A recent preprint introduces a standardized benchmark for measuring factual consistency in RAG pipelines. The authors report a 15 percent reduction in false positives compared to existing heuristic methods. Founders building knowledge bases should note the compute overhead increases by roughly 10 percent.

Researchers from UC Berkeley released a draft on async speculative decoding today. The method allows smaller draft models to verify tokens without blocking the main generation loop. This could significantly reduce cloud compute costs for high-throughput AI applications.

The new RAG orchestration repo utilizes quantized vectors to reduce latency by 40 percent on standard hardware. Stripe AI beta is now available for early access partners with embedded financial data tools. Both releases prioritize local execution over cloud dependencies.

OpenAI dropped GPT-5 this morning. SWE-bench jumped from 71 to 84 percent on first run. Tool use is now native rather than a separate API.

We replaced manual SDR workflows with an autonomous agent stack last month. Customer acquisition cost dropped from $450 to $120 while response rates climbed from 4% to 11%. Stop building features before validating demand with automated pipelines.