Browsing: Analysis

In this tutorial, we build an advanced workflow around Anthropic’s financial-services repository and reproduce its skill-driven architecture in pure Python. We begin by installing the required…

In this tutorial, we explore EdgeBench as a practical benchmark for evaluating advanced AI agents across diverse task categories, runtime environments, and interaction-time budgets. We begin…

In this tutorial, we explore NVIDIA’s srt-slurm framework and learn how we use srtctl to convert declarative YAML configurations into reproducible SLURM benchmark workflows for distributed…

def _ang(bp, L): return math.pi/2 – 2*math.pi*(bp/L) def _pt(bp, r, L): a = _ang(bp, L); return r*math.cos(a), r*math.sin(a) def _arc(s, e, r, L, n=240): if e…

def _hsize(nbytes): for u in [“B”, “KB”, “MB”, “GB”]: if nbytes < 1024: return f”{nbytes:.1f}{u}” nbytes /= 1024 return f”{nbytes:.1f}TB” def build_prompt(instruction, workspace): exts = (“.csv”,…

Introduction systems rarely fail in a single moment. Their performance changes gradually as data distributions shift, calibration drifts, or new patterns emerge in the environment. Eventually…

banner(“STEP 3 — Building the analysis DataFrame”) def process_example(ex): traj = normalize_trajectory(ex.get(“trajectory”)) rc = role_counts(traj) nf, add, dele, _files, _exts = parse_patch(ex.get(“model_patch”)) meta = normalize_metadata(ex.get(“metadata”)) full_text…

print(“Batch scanning the whole corpus (static-only)…\n”) summary_rows = [] all_findings = [] for skill in SKILLS: res = scan(skill, use_llm=False, output_format=”json”) fnds = findings_of(res) summary_rows.append({ “skill”:…

k = RUN_KNOBS train_out = run_cli([“python”,”scripts/train.py”,”–config”,CFG,”–split_dir”,SPLIT, “–optimizer_model”,OPTIMIZER_MODEL,”–target_model”,TARGET_MODEL,”–out_root”,RUN, *COMMON, “train.train_size=0″, f”train.num_epochs={k[‘num_epochs’]}”, f”train.batch_size={k[‘batch_size’]}”, f”gradient.minibatch_size={k[‘minibatch’]}”, f”gradient.merge_batch_size={k[‘merge_batch’]}”, f”gradient.analyst_workers={k[‘workers’]}”, f”optimizer.learning_rate={k[‘lr’]}”, f”optimizer.lr_scheduler={k[‘lr_sched’]}”, “optimizer.use_slow_update=true”, “optimizer.use_meta_skill=true”, f”env.workers={k[‘workers’]}”, f”env.limit={k[‘limit’]}”], “TRAIN (rollout->reflect->aggregate->select->update->gate; slow-update + meta-skill)”) import…

TEXT_COL = “skill_md_content” NUM_COLS = [“skillspector_score”, “static_finding_count”, “skillspector_issue_count”, “virustotal_malicious_count”] TARGET = “clawscan_verdict” def prep(df): out = df.copy() out[TEXT_COL] = out[TEXT_COL].fillna(“”).astype(str).str.slice(0, 6000) for c in NUM_COLS: out[c]…