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Articles by Morgan MA
The Truncated JSON Parsed Fine, So Nobody Noticed
A truncated model response parsed as valid JSON. That is the entire bug. HTTP said 200. The parser said fine. The job wrote a partial patch and moved on. No error line. No stack trace. No alert. Just a quieter, smaller result. I want to trace that shape properly. It is not a model quality problem. It is a completeness problem wearing a success code. I will use a free-tier setup as the running example. Free tiers expose truncation and wall-clock limits quickly.
The Retry That Fired After the Stream Ended
A stream can succeed and still look failed to your client. That gap is where retry logic doubles side effects. The current AI conversation is full of fatigue and agent demos. The useful question is smaller: what did the generated retry wrapper actually do? I'll use a synthetic repro, not a customer incident. The failure is real enough to run on your laptop. It takes one endpoint, one client, and one assertion. The Symptom: Two Jobs for One Submit You hit POST once. The database shows two rows.
The Parser Found a Verdict in the Model's Explanation
My review gate approved a diff the model had rejected. The reject verdict was sitting right there in the output. My parser grabbed an example block instead. That is a twelve-line bug in my own code, not a model failure. I ran the gate against MonkeyCode's free model access and its free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The short version The loop is simple. Generate a patch, ask a model for a structured review, then accept or escalate.
The Short Test Kept a Dead Cache
I will start with the conclusion, not the tool. Caching c_str across growth is a lifetime bug. A short unit test can still pass cleanly. Later growth is what kills the cached pointer. Did your suite ever cross the capacity line? If it did not, the cache only looked stable. A faster coding model does not relax object lifetime. The language rules did not get softer this year. That gap is why this small repro still matters. The symptom Picture a small label type in ordinary service code.
The Warning Died and the Length Field Lied
A silenced narrowing cast can still ship a truncated length. I built a lab repro so you can watch that lie form. Would you have merged the cast just to keep CI green? The conclusion first Reject oversized frames before you store a short length. A green binary is not proof that the length still fits. Ask a model for causes, never for a silencing patch. The symptom I froze The silenced program should print a stored length of 16. The buffer should still hold 65552 payload bytes after the pack.
The Empty Check Passed on a Full Ring
The ring reported empty after a full lap. Full and empty shared one modulus check. I lost every payload and still kept green tests. Why did review miss this quiet bug? The happy path never filled the whole buffer. The Symptom I filled a four-slot ring inside a unit test. I pushed four times and never popped once. Then empty() returned true and dropped the batch. There was no crash and no sanitizer line. CI never failed an assert on this path. The alias is defined behavior, so UBSan stayed quiet.
The Map Dropped a Key When Two Expiry Times Matched
A C++ map can drop a live cache entry. One <= in a comparator is enough. I forced a timestamp tie and watched a key vanish. Would you have caught that in review? This is a debugging retrospective. It is not a product tour. The artifact is a comparator audit you can run locally. The symptom I reconstructed a small TTL cache. Insert looked fine. Lookup looked fine. Unit tests stayed green. Then two entries shared the same expiry second. One key disappeared. No throw. No log line. Just a hole.
Free Models, a Free Server, and a Reference That Outlived Its Function
A dangling reference is easy to miss on your laptop and hard to ignore on a container that behaves differently. Free AI tools can generate the first bug and then help you find it, if you know how to verify their output. This article walks through a real failure: an AI-generated C++ function returned a reference to a local variable. Local tests passed. Then the same code crashed on a remote free server.
The 3% Drop That Only Showed Up on a Clean Server: A Debugging Retrospective
My forwarder lost 3% of its messages. My laptop reported zero. CI reported zero. Staging lost thousands every hour. I blamed the network. I blamed the VM. I blamed everything except my read loop. This is the retrospective: one wrong assumption, three counters, a twelve-line fix. The Symptom The service was a small TCP forwarder. Accept a connection, read text lines, forward them. Nothing exotic. Staging showed the loss: 97,004 lines in, 94,183 lines out. No errors. No exceptions. Just missing messages.
The Fix That Passed Review and Crashed in Release
The fix passed review. It still crashed in release. The reviewer was never tested. The reviewer was me. And an AI model. We both approved the same wrong patch. This is the retrospective. Symptom, root cause, fix. Plus the debugging loop that actually caught it. The Symptom A small C++ worker pool returned garbage. Not always. Only with -O2. Only after a few thousand tasks. That is the classic undefined behavior profile. The code looks innocent. The compiler does something legal.
The Coverage Loop: Turning Free AI Tokens into Verified C++ Tests
Dean Lee Real-time AI & tech analysis through economics & game theory. Follow the incentives: who wins, who pays, what happens next. Master's in Business, CUHK Email Education Master’s in Business, The Chinese University of Hong Kong (CUHK) Pronouns he/him Work Quantitative finance professional in stochastic models and option pricing. Joined
A systematic review and meta-analysis of randomized controlled trials for physical activity among colorectal cancer survivors: directions for future research
In summary, this study assessed the effectiveness of PA interventions for CRC survivors in improving their PA levels and aimed to explore the appropriate PA patterns for designing PA interventions for such populations. The results of meta-analysis showed that PA interventions have a small-to-moderate (SMD = 0.32, 95% CI [0.09–0.54], Z = 2.79, p = 0.005) effect on increasing the total PA levels for CRC survivors.
Lactate-dependent transcriptional regulation controls mammalian eye morphogenesis
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