🤖 AI writes code faster, but are we producing vulnerabilities faster? With AI generating over 40% of new code, traditional post-scanning just can't keep up. That’s why we built Qoder Security—shifting security left directly into the coding session. The Impact: • +60% vulnerability detection rate • -80% false-positive alerts • Hours to fix, not weeks From real-time regex interception to cross-file deep review, we’re embedding security natively into your workflow. 🚀Security starts with the first line of code. Try it out in Qoder Desktop & CLI today! Qoder Desktop (IDE) guide:  Qoder CLI guide:  #Qoder# #AICoding# #QoderSecurity# #CyberSecurity# #AgenticCoding#

We moved away from @cursor_ai to @claudeai Code and Codex. Not because Cursor isn't good it is. But if you're on the bleeding edge, you're moving toward coding agents that don't need an IDE at all. Here's what that has actually changed at Multimodal: The workflow used to be: engineer understands the requirement, engineers writes the code, engineer reviews the PR, repeat. The workflow now is: agentic coding drafts the PRD from existing code and roadmap context, creates the Linear tickets, writes the implementation, generates the PR stack. The engineer reviews, redirects, architects. The ratio of human judgment to human execution has inverted. We run Claude Code and Codex in tandem — they're not the same tool. One plans, the other reviews. One drafts, the other stress-tests. The ping-pong between them catches things neither would alone. What this has meant in practice: we're hitting our product roadmap materially faster with the same team size. We measure it in two ways compression in time-to-release for product features, and PR volume normalized for headcount. The honest framing: the constraint isn't gone, it's just shifted. You still need engineers who can think architecturally, who know when the agent is wrong, who can define what "done" actually means. Those people are now 5-10x more productive. The engineers who can't operate at that level who still want to write every line themselves are increasingly misaligned with how software gets built now. That's not a comfortable thing to say. But it's what's true. #AgenticCoding# #AI# #Startups# #Engineering# #ClaudeCode#

Agentic coding means you can ship far, far faster It also means though it can be tough to know when to stop on a release Just one more feature, one more fix, one more improvement. It will only take an hour or so, not a month. (QA and regressions aside, of course)

Agentic coding is a leap toward a Star Trek future in which you can simply say "Computer, do ___" and it executes an arbitrarily complex task. We just need to make it a few orders of magnitude faster and insert tons of safeguards.

Agentic coding is a huge boon for software developers that want to get far more done, great for IT people to build vastly more custom systems internally, great for domain experts that want to automate workflows or wire systems together, and absolutely fantastic for anyone curious to learn how to start coding. What it’s less great for is casually building complex software that you have to maintain on an ongoing basis and take on all the risk for. Upgrades, maintenance, keeping up to date with latest security issues, and so on, are taxes most knowledge workers aren’t familiar with or prepared for. Net net: we’re going to get 100X more software and vastly more software developers in the future. But that’s different from *everyone* rolling their own.

lazymaxxing agentic coding: When i have multiple agents working on the same code repo, if there's conflicts/overlaps, they just figure it out amongst themselves? I want to have 8 tabs going on new features, and the agents just all auto handle the merging/testing/git/conflicts/etc without complaining about each other

Quantized Qwen3.8 27B for agentic coding: Same Score, 42 Different Outcomes.  I’ve been evaluating quantized Qwen3.8 27B models on DeepSWE 1.1, but I’m giving up. 4-bit AWQ run #1#: 38.94% Reward 4-bit AWQ run #2#: 31.86% Reward 32 of the 113 task outcomes flipped: 20 previously solved tasks failed, while 12 previously failed tasks passed. On non-agentic tasks, I’ve found NVIDIA NVFP4 performs very closely to AWQ. On DeepSWE 1.1? NVIDIA NVFP4: 31.86% Reward Exactly the same score as AWQ run #2#, but 42 task outcomes flipped (nearly one-third of the benchmark!). Each solved 36 tasks, with only 15 successes in common. For reference, BF16 with the same Pi harness and thinking mode reached 43.36%. Quantization certainly contributes to trajectory instability: small changes in token probabilities can alter an edit or tool call. That changes what the agent sees next, potentially sending subsequent decisions down a different path. Errors can compound across turns. Separating that from ordinary sampling variability requires repeated experiments. I’d want at least three independent full runs per model with fixed settings before drawing conclusions, and more for a reliable estimate of variability. Too long, too expensive. Don’t trust anyone reporting a quantized model’s accuracy on agentic tasks without also reporting the run-to-run standard deviation.

Nex AGI's agentic N2.5 family is live on OpenRouter: Nex-N2.5-Pro and Nex-N2.5-Mini. Both are built to turn goals into working, verified outcomes, with agentic coding as the core strength. Tool calling and adjustable reasoning effort on both. Free for a limited time ↓