Developer shares key lessons from building AI into real-world SaaS workflows

·3 views

A developer building AI-powered SaaS applications has shared practical lessons learned while integrating large language model APIs into a Smart Upload workflow for energy and compliance data. The experience revealed that connecting an LLM API is straightforward, but designing reliable, context-aware workflows around it is far more complex. Key takeaways include the importance of not letting raw AI output directly become application data, treating human corrections as valuable feedback, and providing the AI with rich contextual information such as user role, organisation, and existing data. The developer also emphasised that standard software engineering practices remain essential when building AI features. The overarching conclusion was that effective AI products depend less on generating answers and more on fostering structured collaboration between AI systems, application data, and users.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

SaveNowX streams X videos to users without ever storing files on its servers

A developer has built SaveNowX (savenowx.com), a free tool that lets users download videos from public X (formerly Twitter) posts without requiring an account, adding watermarks, or keeping any download history. The core architectural principle is that no video data is ever written to the server's disk — the backend resolves post URLs and retrieves CDN links without handling video bytes directly. For standard MP4 videos, a Cloudflare Worker intercepts the request solely to add a Content-Disposition header, ensuring the file downloads directly from X's own CDN to the user's device. For HLS-only videos — which exist as playlist files and multiple small segments rather than a single MP4 — a lightweight job system fetches, remuxes, and streams the file to the user, deleting any temporary data immediately after delivery. Caching rules are also in place to prevent repeated bad requests from generating unnecessary backend load.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Debugging AI failures requires asking 'which layer' broke, not just 'where'

Software engineer Derek Wang argues that when an AI system fails, the instinct to pinpoint a single broken location is a fundamental debugging trap. He proposes a three-layer analysis framework: L1 (the visible phenomenon), L2 (structural flaws in the code), and L3 (flawed design assumptions underlying the system). A key rule in his approach forbids labelling something a 'data problem' as a root cause, since that label merely restates the symptom without explaining why the data went wrong. Wang also warns that fixing only the identified layer often triggers failures elsewhere, as engineering systems are deeply interconnected. The framework functions as a mandatory gate — no fix is permitted until all three layers have been examined and documented.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

CVSS, EPSS and KEV: A Practical Framework for Prioritizing Security Vulnerabilities

Dependency scanners routinely surface hundreds of vulnerabilities, but fewer than 5% of published CVEs are ever exploited in the wild, making raw CVSS scores an unreliable basis for prioritization. CVSS rates theoretical severity on a 0–10 scale but ignores real-world exploitation likelihood, meaning a high score can reflect a risk that attackers never actually pursue. EPSS, a machine-learning model maintained by FIRST, addresses this gap by producing daily probability scores for exploitation within 30 days, with the top 1% of CVEs by EPSS accounting for roughly 75% of observed attacks. CISA's Known Exploited Vulnerabilities catalog goes further, listing CVEs confirmed as actively exploited in real attacks — these entries represent the highest-priority fixes regardless of their CVSS score. Used together, the three systems allow security teams to dramatically shrink fix lists while concentrating effort on vulnerabilities that pose genuine, immediate risk.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

IP Reputation Alone Cannot Catch Residential Proxies, Research Shows

Residential proxies route traffic through ordinary consumer internet connections, making them nearly indistinguishable from legitimate household users by traditional IP reputation systems. Proxy infrastructure enters homes via bandwidth-sharing apps, compromised routers, Android TV boxes, or SDKs hidden in app terms of service. According to proxy-tracking platform Synthient, residential proxy traffic in May 2026 touched roughly 9.2 million unique domains, with streaming services, financial platforms, and e-commerce sites receiving a disproportionate share. Observed abuse included ad fraud, credential stuffing, account takeover, automated purchasing, and large-scale scraping. Security researchers argue that effective detection requires session-level behavioral analysis and time-stamped provider attribution, rather than static IP blacklisting alone.

0 comments Read more at DEV Community