I built Rudoc because I wanted a smaller tool for simple file convertion workflow. (Pandoc was so heavy for me) I once wanted to try Pandoc, but after I downloaded it from GitHub, I found that it is around 70 MB. It also needed a lot of dependencies. Rudoc is not a substitue of Pandoc, it supports limited set of formats (only txt, md, html, typ, docx, pdf, pptx, xml, json) and doesn't support advanced formatting. The most common workflow is just turn a markdown file to a html. PDF generation can

When I started building my Maternity HL7-to-FHIR Pipeline , my first instinct was to do everything in Python. Parse the HL7 message, map the fields, validate the FHIR resource, persist it, all in one FastAPI service. It was clean. It was simple. It was wrong. The "Just Parse It in Python" Phase My initial architecture looked like this: Hospital System --MLLP--> Python Script --> HAPI FHIR Server I used python-hl7 to split messages on | and count field positions. For a single ADT^A01 (patient admission) message, it worked fine. I could pull the patient name from PID-5 , the MRN from PID-3 , the gender from PID-8 , and build a FHIR Patient resource from it. Then I tried a real-ish maternity workflow (an admission, an order, and a set of vitals) and things fell apart quickly. Five Problems That Changed My Mind 1. MLLP Is Not HTTP Hospital systems don't send HL7 over HTTP. They send it over MLLP (Minimum Lower Layer Protocol), which is a TCP socket protocol with specific framing bytes ( \x0b at the start, \x1c\x0d at the end). The sender expects an ACK or NACK response in HL7 format, not an HTTP status code. Building an MLLP listener in Python is possible . Libraries like aioml7 exist. But you're now maintaining a custom TCP server alongside your HTTP API server, handling connection pooling, timeouts, and HL7 acknowledgment generation. That's a lot of infrastructure code that has nothing to do with your actual transformation logic. Mirth Connect handles MLLP natively. You point it at a port, it listens, it parses, it ACKs. Done. One config screen, no custom code. 2. HL7 Parsing Is Messier Than It Looks The pipe-delimited format looks simple: PID|1||1234567^^^MRN||TEST^PATIENT^MARY^^MS||19920315|F|||14 SAMPLE ST^^SYDNEY^NSW^2000^AU But consider: Component separators : PID-5 is TEST^PATIENT^MARY^^MS , which is family, given, middle, suffix (empty), prefix. Miss the empty suffix and your prefix ends up as the suffix. Repeating fields : PID-3 can contain multiple identifier

Claude Opus 5 is out, and Artificial Analysis — who supported Anthropic's pre-release evaluation — just dropped their full benchmark breakdown. The headline: new top model for agentic knowledge work, and cheaper per task than Fable 5. That combination doesn't come along often at the frontier. "Opus 5 (max) scores 61 on the Artificial Analysis Intelligence Index, effectively tied with Claude Fable 5 (max, 60), and ahead of GPT-5.6 Sol (max, 59)" What actually changed New agentic leader: 1861 Elo on GDPval-AA v2 — more than 100 points ahead of both Fable 5 and GPT-5.6 Sol. On AA-Briefcase (agentic knowledge work), it's +146 Elo over Fable 5. Joint first on coding: Opus 5 (xhigh) with Claude Code tops the Artificial Analysis Coding Index, including the highest score on SWE-Atlas-QnA. 89% on Terminal-Bench v2.1: Roughly in line with the current terminal leader, GPT-5.6 Sol. Cost per task: $2.03 at max effort — vs Fable 5's $2.75. That's 26% less for equivalent or better intelligence on agentic benchmarks. 1M token context window (same as Opus 4.8), 5 effort settings (low → max), and server-side fallback support. Pricing: $5/$25 per million input/output tokens — same rate as previous Opus launches. The cost-intelligence shift For agentic workloads — the things most teams are actually building on right now — Opus 5 doesn't just match Fable 5. It beats it, and charges less to do it. Fable 5 was the "throw more at it" option. Opus 5 reframes the trade-off: better agentic outcomes and a lower bill. At mid-tier effort settings (high, xhigh), it can outperform both Opus 4.8 and Sonnet 5 on a cost-per-task basis. That's a lot of headroom to play with before you're even at max effort. The caveat worth flagging: factual knowledge still lags. Opus 5 improved +7 points on AA-Omniscience over Opus 4.8, but its hallucination rate climbed 14 points to 50% — it guesses more confidently when uncertain. For retrieval-heavy or factual precision tasks, Fable 5 still holds the edge. What to

"You're spawning a fresh codex exec for every single call? Just run the app-server and reuse a thread. The prompt cache alone will pay for it." That sentence sounds so obviously right that nobody ever benchmarks it. We run a small bazaar of headless AI daemons — designers, reviewers, code workers — and each one shells out to OpenAI's Codex CLI dozens of times per work item. A resident codex app-server --stdio with warm threads looked like free money. We measured it four times. The prompt cache eventually hit 86% on the turns that mattered. The pipeline got slower than the boring baseline, and 39% more expensive in raw tokens. This is the story of why, and of what app-server is actually for. The setup was deliberately minimal: no resident daemon, no pool. The server is spawned privately for one formation , runs its turns, and is killed by a context manager on the way out — success or crash. That keeps the comparison honest: same lifecycle as a subprocess, and adopting it stays a measurement problem instead of an architecture debate. Trusting the probe Before writing any integration, we cloned the actual openai/codex source and pointed a probe at a real app-server: handshake, per-turn sandbox overrides, interrupts, forks, kill-and-resume. Eight checks, all green, plus three findings you won't find in any docs: cwd and sandboxPolicy are sticky when omitted — every turn must re-state them explicitly, or turn 3 silently inherits turn 2's write access. Token usage arrives on thread/tokenUsage/updated , not on turn/completed . An empty thread that never ran a turn is never materialized — you cannot resume it later. Pre-warming a thread pool is a trap. And the seductive one: on the probe's trivial two-turn thread, turn 2 reported 15,104 cached input tokens . See? The cache works. Ship it. That number cost us three more measurement runs to un-believe. The four runs Same fixture repo, same greenfield request, one real four-stage formation each: A: codex exec (baseline) B: app