LangExtract: Google’s Gemini-Powered Library That Turns Raw Text into Reliable Data

 

A new way to mine insight from messy text

On July 30 2025 the Google Developers Blog unveiled LangExtract, an open-source Python package that promises to “unlock the data within” any text-heavy corpus, from clinical notes to customer feedback threads. Built around Gemini models but compatible with any LLM, the project aims to replace brittle regex pipelines with a single declarative interface for extraction, visualization and traceability. 

Why LangExtract stands out

LangExtract combines seven features that rarely appear together in one tool:

1. Precise source grounding – every entity you pull out is linked back to its exact character span in the original document, so auditors can see where a value came from.

2. Schema-enforced outputs – you describe the JSON you want, add a few examples, and the library leverages Gemini’s controlled generation to keep responses on-spec.

3. Long-context optimisation – chunking, parallel passes and multi-stage recall tame “needle-in-a-haystack” searches across million-token inputs.

4. Interactive HTML visualisation – one command turns results into a self-contained page where extractions glow inside the source text.

5. Flexible back-ends – swap Gemini for on-device Ollama models or any OpenAI-compatible endpoint.

6. Domain agnosticism – the same prompt-plus-examples recipe works for finance, law, medicine or literature.

7. Apache-2.0 licence – no gating, just pip install langextract. 

How it works in practice

A “quick-start” script pulls Shakespeare characters, emotions and relationships in about a dozen lines of code, then writes an interactive HTML overlay showing each extraction highlighted inside the play. The same pattern scales: push the full Romeo and Juliet text through three extraction passes and LangExtract surfaces hundreds of grounded entities while keeping recall high. G

The GitHub repository already counts 200+ stars less than a week after launch, and ships with examples for medication extraction and structured radiology reporting—fields where provenance and accuracy are critical. A live Hugging Face demo called RadExtract shows the library converting free-text X-ray reports into structured findings, then color-coding the original sentences that justify each data point. 

Under the hood: Gemini plus controlled generation

When you pass model_id="gemini-2.5-flash" (or -pro for harder tasks), LangExtract automatically applies Google’s controlled generation API to lock output into the schema you defined. That means fewer JSON-parse errors and cleaner downstream pipelines—something traditional LLM calls often fumble. For massive workloads, Google recommends a Tier-2 Gemini quota to avoid rate limits. 

Why developers should pay attention

Information extraction has long oscillated between hand-tuned rules (fast but brittle) and heavyweight ML pipelines (accurate but slow to build). LangExtract offers a third path: prompt-programming simplicity with enterprise-grade traceability. Because it’s open-source, teams can audit the chain of custody and fine-tune prompts to their own compliance rules instead of black-box vendor filters.

Whether you’re structuring earnings calls, tagging sentiment in product reviews, or mapping drug-dosage relationships in EMRs, LangExtract turns unreadable text into queryable data—without sacrificing transparency. For AI enthusiasts, it’s also a practical showcase of what Gemini’s long-context and schema-control features can do today.

Bottom line: install the package, craft a clear prompt, add a few gold examples, and LangExtract will handle the rest—from parallel chunking to an HTML dashboard—so you can move straight from raw documents to actionable datasets.

ChatGLM’s GLM‑4 family levels up—and brings its own toolbox

 Tsinghua‑spun Zhipu AI has spent three years iterating on ChatGLM, a Chinese‑English rival to GPT. Its new report zooms in on the GLM‑4 series, a trio that stretches from a data‑center‑class behemoth to a 9 B‑parameter fine‑tune you can run at home. The headline: GLM‑4 “closely rivals or outperforms GPT‑4” on marquee leaderboards—while an All Tools variant autonomously fires up external apps to finish harder jobs. 

Under the hood

Piece	Why it matters
10 T‑token corpus (Chinese & English‑heavy, 24 other languages)	Gives the model near‑par bilingual parity—something GPT‑4 still chases in Chinese.
Multi‑stage alignment (SFT → RLHF)	Drives instruction following to GPT‑4‑Turbo levels on IFEval without bloating answers.
All Tools post‑training	Lets GLM‑4 decide if a prompt needs web search, Python, text‑to‑image, or any user‑defined API—no manual tool triggers.

The SKUs

• GLM‑4 – flagship ~130 B active params, 128 K context, up to 1 M with sparse attention.

• GLM‑4‑Air – latency‑trimmed 34 B variant tuned for GPU serving.

• GLM‑4‑9B / 9B‑Chat – consumer‑grade checkpoint (128 K / 1 M context) already live on Hugging Face.

Scorecard highlights

• General reasoning: beats or ties GPT‑4 on MMLU, GSM8K, MATH, BBH, GPQA, HumanEval. 

• Chinese alignment: tops GPT‑4 on AlignBench. 

• Long context: matches GPT‑4‑Turbo 128 K and Claude 3 at 256 K spill‑tests. 

• Tool use: in dev‑set trials, GLM‑4 All Tools edges GPT‑4 All Tools in web‑info retrieval and Python‑powered math. a

Why it matters

1. Bilingual crown – China finally has an open(-ish) model that doesn’t trade English chops for Mandarin mastery.

2. Tool autonomy – A single checkpoint that chooses whether to browse, code or draw marks a step toward plug‑and‑play agent workflows.

3. Open‑source momentum – Previous ChatGLM releases logged 10 M+ Hugging Face downloads in 2023; GLM‑4‑9B is expected to super‑charge that hobbyist wave. 

Rapid timeline of the GLM ecosystem

![timeline figure omitted] The paper’s timeline shows an 18‑month sprint from GLM‑130B to GLM‑4‑All Tools, with side quests into code (CodeGeeX), vision (GLM‑4V‑9B) and agents (AutoWebGLM). 

The road ahead

Zhipu AI hints at an MoE‑style GLM‑5 and deeper tool libraries (SQL, vector search, proprietary APIs). For builders already juggling browser calls, Python sandboxes and image pipes, GLM‑4 All Tools may offer a cleaner, unified brain—especially if your product needs to speak both English and Mandarin with equal poise.

Paper link: arXiv 2406.12793 (PDF)

ARC‑Hunyuan‑Video‑7B: Tencent’s bid to finally understand short videos

 Swipe through WeChat Channels or TikTok and you’ll find an AI nightmare: jump‑cuts, dense captions, meme audio and zero breathing room. Generic vision‑language behemoths struggle to keep up. ARC‑Hunyuan‑Video‑7B—unveiled this week by Tencent’s ARC Lab—takes direct aim at that chaos with an end‑to‑end, tri‑modal stack that ingests RGB frames, raw audio and ASR text before producing structured outputs.

What makes it different?

Design choice	Why it matters
Audio encoder fused with ViT backbone	Lets the model pinpoint punch‑lines, product mentions or sudden sound cues that pure‑vision systems miss.
Timestamp overlay on every frame	Gives the LLM hard temporal anchors for grounding, enabling second‑level captions and event logs.
Automated annotation pipeline with “millions” of real shorts	Avoids domain shift that plagues models trained on movie trailers or HowTo videos.
Five‑stage training (pre‑train → SFT → cold‑start → RLHF → SFT)	RL on objective tasks (e.g., exact‑match grounding) unlocks the subjective “explain the joke” style understanding.

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Early numbers

• ShortVid‑Bench (new internal benchmark): authors report “strong performance” across captioning, QA, grounding and reasoning—outpacing prior open models, though exact deltas remain embargoed. 

• Latency: stress tests show 10 s end‑to‑end for a 60‑second clip on an H20 (≈A100‑class) GPU—fast enough to power feed ranking or real‑time moderation. 

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Why builders should care

1. One stop for multi‑task video NLP – The same checkpoint handles highlight extraction, event logs, scene QA and clip‑level summaries, reducing pipeline sprawl.

2. Audio is first‑class – Brands, educators and accessibility teams can finally query both what’s shown and what’s said in user‑generated shorts.

3. Edge‑friendly – At 7 B parameters (≈8.6 B in BF16), it’s small enough for a single A100 or dual consumer GPUs under vLLM. 

4. Open weights & code – Hugging Face repo, training scripts and a vLLM deployment guide are already public, licensing‑friendly for commercial use. 

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The bigger picture

OpenAI’s GPT‑4o Vision and Google’s Gemini 1.5 Pro handle long vids but lean on frame sampling and text prompts. ARC‑Hunyuan‑Video‑7B instead streams raw pixels + sound and returns a structured JSON‑style digest—closer to what feed‑ranking or search engines need. Tencent claims the model is already in production, lifting short‑video engagement metrics; if those gains hold, expect other platforms to pivot toward structured rather than free‑text video understanding.

Paper link: arXiv 2507.20939 (PDF)

Align Evals: LangSmith’s New Playground for Human‑Aligned LLM Evaluation

When LangChain announced Align Evals on July 29, 2025, it answered a pain point that has dogged almost every LLM team: evaluator scores that don’t line up with human judgment. The new feature—now live for all LangSmith Cloud users—lets builders calibrate their “LLM‑as‑a‑judge” prompts until automated scores track closely with what real reviewers would say. 

Why alignment matters in evaluation

Even the best prompt tweaks or model upgrades lose value if your test harness misfires. LangChain notes that teams waste time “chasing false signals” when evaluators over‑ or under‑score outputs versus human reviewers. Align Evals gives immediate feedback on that gap, quantifying it as an alignment score you can iterate against. 

A feature set built for rapid iteration

Align Evals drops a playground‑style interface into LangSmith with three marquee capabilities:

1. Real‑time alignment score for each evaluator prompt revision.

2. Side‑by‑side comparison of human‑graded “golden set” examples and LLM‑generated scores, sortable to surface the worst mismatches.

3. Baseline snapshots so you can track whether your latest prompt improved or regressed alignment. 

The alignment flow in four steps

LangChain distills evaluator creation into a structured loop:

1. Select evaluation criteria that reflect app priorities (e.g., correctness and conciseness for chatbots).

2. Curate representative data—good and bad outputs alike—to form a realistic test bed.

3. Assign human scores to create a gold standard.

4. Draft an evaluator prompt, run it against the set, and refine until its judgments mirror the human baseline. The UI highlights over‑scored or under‑scored cases so you know exactly what to fix next. 

Availability and roadmap

Align Evals is already shipping in LangSmith Cloud; a self‑hosted release drops later this week. Looking ahead, LangChain teases analytics for long‑term tracking and even automatic prompt optimization that will generate alternative evaluator prompts for you. 

Why AI builders should care

Evaluations are the backbone of continuous improvement—whether you’re evaluating a single prompt, a RAG pipeline, or a multi‑agent workflow. Yet teams often discover that a “99 % accurate” evaluator still lets bad outputs slip through. Align Evals lowers that friction, turning evaluator design into a measurable, repeatable process.

For AI enthusiasts and practitioners, the message is clear: before you chase bigger models or flashier agents, make sure your evaluators speak the same language as your users. With Align Evals, LangChain just handed the community a calibrated mic—and the feedback loop we’ve been missing.

PhyWorldBench asks: can your video model obey gravity?

 Text-to-video (T2V) generators can paint dazzling scenes, but do they respect momentum, energy conservation—or even keep objects from phasing through walls? PhyWorldBench says “not yet.” The new 31-page study introduces a physics-first benchmark that pits 12 state-of-the-art models (five proprietary, seven open source) against 1,050 carefully curated prompts spanning real and deliberately impossible scenarios. The verdict: even the best models fumble basic mechanics, with the proprietary Pika 2.0 topping its class at a modest 0.262 success rate, while Wanx-2.1 leads open source. 

A benchmark built like a physics textbook

Researchers defined 10 main physics categories, each split into 5 subcategories, then wrote 7 scenarios per subcategory—and for every scenario, three prompt styles (event, physics‑enhanced, detailed narrative). That’s how you get to 1,050 prompts without redundancy. 

Anti‑physics on purpose

One twist: an “Anti‑Physics” track where prompts violate real laws (e.g., objects accelerating upward). These gauge whether models blindly mimic training data or can intentionally break rules when asked. 

Cheap(er) scoring with an MLLM judge

Instead of hand‑labeling 12,600 generated videos, the team devised a yes/no metric using modern multimodal LLMs (GPT‑4o, Gemini‑1.5‑Pro) to check “basic” and “key” physics standards. Large human studies back its reliability, making large‑scale physics eval feasible. 

What tripped models up

• Temporal consistency & motion realism still break first.

• Higher‑complexity composites (rigid body collisions, fluids, human/animal motion) expose bigger gaps.

• Models often follow cinematic cues over physics, picking “cool” shots that contradict dynamics. 

Prompting matters (a lot)

Richer, physics‑aware prompts help—but only so much. The authors outline prompt‑crafting tips that nudge models toward lawful motion, yet many failures persist, hinting at architectural limits. 

Why this matters

• Reality is the next frontier. As T2V engines head for simulation, education and robotics, looking right isn’t enough—they must behave right. 

• Benchmarks drive progress. Prior suites (VBench, VideoPhy, PhyGenBench) touched pieces of the problem; PhyWorldBench widens coverage and difficulty, revealing headroom hidden by softer tests. 

• MLLM evaluators scale oversight. A simple, zero‑shot judge could generalize to other “lawfulness” checks—chemistry, finance, safety—without armies of annotators. 

The authors release all prompts, annotations and a leaderboard, inviting labs to iterate on physical correctness—not just prettier pixels. Until models stop dropping balls through floors, PhyWorldBench is likely to be the scoreboard everyone cites.

Paper link: arXiv 2507.13428 (PDF)

MegaScience formalizes science reasoning data—and smaller models suddenly look smarter

 Open-source LLMs can do math and code, but ask them to reason through a physics word problem or a cell-biology puzzle and they wobble. The GAIR Lab at Shanghai Jiao Tong University thinks the culprit is data, not architecture. Their new paper introduces TextbookReasoning (650 k Q&A pulled from 12 k university textbooks) and MegaScience (a 1.25 M‑sample mix of cleaned public science sets), then shows that models post‑trained on these datasets outperform their own official instruct variants—while using far shorter responses. 

The problem: bad science data, bad evals

Most “science” corpora rely on noisy web text, weak decontamination and multiple‑choice benchmarks that don’t probe true reasoning. The authors flag four pain points: unreliable benchmarks, flimsy leakage checks, low‑quality references and shallow CoT distillation. 

Two datasets, one pipeline

• TextbookReasoning – 650 k verified questions across seven disciplines (physics → economics), built via textbook digitization, QA pair extraction, deduping, refinement and LLM‑assisted decontamination. 

• MegaScience – 1.25 M high‑quality instances from NaturalReasoning, Nemotron‑Science and TextbookReasoning, curated with a three‑way selection scheme: response‑length, difficulty, and random sampling, plus solution annotation. 

Notably, answers are short: 410 tokens (TextbookReasoning) and 721 tokens (MegaScience) on average—meaning cheaper training and inference than CoT-heavy rivals. 

Proof in the checkpoints

Fine‑tuning Llama3.1, Qwen2.5 and Qwen3 base models on MegaScience consistently beats their official instruct models across “general,” “specific,” and “math” categories. Example: Qwen3‑30B jumps from 55.66 → 61.12 average, with math rising to 89.33. 

Ablations back the pipeline: drop refinement and performance collapses (58.33 % → 13.15 % overall); remove the extra CoT step and scores slide to 57.33 %. Decontamination matters too—without it, leakage inflates averages to 58.57 %. 

Why this matters

• Science is more than math/code. The field lacked open, verifiable, long‑form reasoning sets; MegaScience fills that gap. 

• Shorter CoT ≈ cheaper scaling. The datasets’ concise answers let bigger models benefit more from fine‑tuning—hinting at a “scaling law for data efficiency” in science domains. 

• Open everything. The team releases the full curation pipeline, eval system, seven trained models and all datasets, inviting the community to iterate. 

If your lab is chasing AI scientists rather than chatty coders, MegaScience is a ready-made jumpstart—and a reminder that better questions and cleaner answers can beat another billion tokens of sludge.

Paper link: arXiv 2507.16812 (PDF)

RoGuard 1.0: Roblox’s Open-Source Guardrail LLM Raises the Bar for Safe Generation

 When Roblox quietly pushed RoGuard 1.0 to Hugging Face, it wasn’t just another model drop—it was a statement that safety tooling can be both state-of-the-art and open. Built on top of Llama‑3.1‑8B‑Instruct, RoGuard is an instruction‑tuned classifier that decides whether a prompt or a model’s reply violates policy—covering both ends of the conversation loop. 

Google, Meta, NVIDIA, OpenAI—pick your favorite heavyweight; Roblox claims RoGuard is beating their guardrail models on leading safety benchmarks, from Llama Guard and ShieldGemma to NeMo Guardrails and GPT‑4o. That’s a bold flex, backed by F1 scores across a mix of in‑domain and out‑of‑domain datasets. 

Dual-layer defense, single lightweight core

Most moderation stacks bolt together multiple filters. RoGuard streamlines that: one 8B‑parameter model, two checkpoints of scrutiny—prompt and response. This dual‑level assessment matters because unsafe content doesn’t just come from users; it can leak from the model itself. 

Data done right (and openly)

Roblox emphasizes no proprietary data—only synthetic and open-source corpora tuned to diverse safety taxonomies. They even sprinkle in chain‑of‑thought rationales so the model learns to justify its calls, not just spit out “violation” labels. The result: stronger generalization and clearer internal reasoning. 

Benchmarks, but with context

RoGuard isn’t a single leaderboard cherry-pick. Roblox released RoGuard‑Eval, a 2,873‑example dataset spanning 25 safety subcategories, hand‑labeled by policy experts and adversarially probed by internal red teams. Reporting in binary F1 keeps things honest and comparable, and the model still leads. 

Why builders should care

If you’re wiring generative text into games, chatbots, or UGC platforms, moderation often becomes a patchwork of regexes, keyword lists, and black-box APIs. RoGuard’s Apache‑friendly weights (via OpenRAIL license) let you self‑host a modern guardrail without vendor lock‑in—and fine‑tune it to your own taxonomy tomorrow. 

Plug, play, and iterate

Weights live on Hugging Face; code and eval harness sit on GitHub. Spin up inference with any OpenAI‑compatible stack, or slot RoGuard in front of your generation model as a gating layer. Because it’s an 8B model, you can realistically serve it on a single high‑RAM GPU or even CPU clusters with batching. 

The bigger picture

We’re entering an era where “safety” can’t be an afterthought—especially as APIs enable unlimited text generation inside social and gaming ecosystems. By open‑sourcing both the toolkit and the yardstick, Roblox invites the community to audit, extend, and pressure-test what “safe enough” really means. 

RoGuard 1.0 shows that thoughtful guardrails don’t have to be proprietary or flimsy. They can be transparent, benchmarked, and built to evolve—exactly what AI enthusiasts and responsible builders have been asking for. Now the ball’s in our court: fork it, test it, and make the open internet a bit less chaotic. 

ThinkAct lets robots “think, then act” — and the payoff is new SOTA across embodied AI benchmarks

 Anyone who has watched today’s end‑to‑end robot policies fail a complex kitchen task knows the weakness: they map pixels to motors with no explicit plan. ThinkAct flips that script. The NTU‑NVIDIA team behind the paper trains a multimodal LLM to write a high‑level reasoning plan, turns that plan into a compact visual‑plan latent, then hands it to a DiT‑based action model that executes at control‑loop speed. The result is an agent that deliberates like GPT‑4o yet moves with the reactivity of classic policies.

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How ThinkAct pulls it off

Component	What it does	Why it matters
Reinforced visual latent planning	Rewards the reasoning LLM with goal‑completion and trajectory‑consistency signals derived from vision, forcing plans that actually work in the scene.	Bridges abstract language plans to pixel‑level feedback.
Visual‑plan latent	Compresses the entire chain‑of‑thought into a fixed‑size latent that conditions a frozen DiT policy.	Keeps the policy lightweight and allows asynchronous slow‑think / fast‑act loops.
Dual‑system inference	LLM thinks a few times per second; the action model ticks every 20 ms.	Yields real‑time control without sacrificing deliberation.

Benchmark sweep at two skill levels

Suite	Metric	Prev SOTA	ThinkAct
EgoPlan‑Bench2	Acc. ↑	Qwen 2.5‑VL* 66.3	71.4
RoboVQA	Acc. ↑	Qwen 2.5‑VL* 63.5	69.2
OpenEQA	Acc. ↑	OpenVLA 52.1	57.8
SimplerEnv (manip.)	Succ.% ↑	DiT‑Policy 45.2	62.7
LIBERO (manip.)	Succ.% ↑	OpenVLA 48.9	60.3

Qwen 2.5‑VL numbers are the authors’ fine‑tuned baseline.

Few‑shot powers

With just 5–10 demos per LIBERO task, ThinkAct’s policy finetunes to new objects and layouts, beating OpenVLA by 9–12 points.o

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Why this matters

• Plan‑centric embodied AI. ThinkAct shows that giving agents an explicit, reward‑aligned plan latent trumps opaque end‑to‑end policies for long‑horizon tasks.

• Self‑reflection in the loop. The reasoning LLM can detect a failure mid‑episode, revise its latent plan, and rescue the run — a first for open‑source VLA systems.

• Few‑shot deployment. Labs can adapt to a new kitchen or warehouse with handfuls of tele‑op traces instead of days of retraining.

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ThinkAct’s code is coming soon, but the project page already hosts videos of robots closing drawers, shifting condiments and answering environment‑specific questions after reasoning out loud. The message is clear: future embodied agents won’t just map images to torque — they’ll think, decide why, then act.

Paper link: arXiv 2507.16815 (PDF)

Gemini 2.5 Flash‑Lite Hits GA: Google’s Fastest, Most Affordable Gemini Model Yet

 

A lightning‑quick sibling joins the Gemini lineup

On July 22, 2025 Google formally declared Gemini 2.5 Flash‑Lite stable and generally available (GA), rounding out the 2.5 family after Pro and Flash graduated last month. Flash‑Lite is engineered to be both the fastest and cheapest Gemini variant, costing $0.10 per million input tokens and $0.40 per million output tokens—the lowest pricing Google has ever offered for a first‑party model. 

Why “Lite” isn’t lightweight on brains

Despite its budget focus, Flash‑Lite pushes the “intelligence‑per‑dollar” frontier thanks to an optional native reasoning toggle. Builders can keep latency razor‑thin for classification or translation and only pay extra compute when deeper chain‑of‑thought is required. The model also ships with Google’s controllable thinking budgets, letting developers fine‑tune response depth via a single parameter. 

Feature set at a glance

• One‑million‑token context window: The same massive prompt length as Gemini 2.5 Pro—ideal for large documents, multi‑day chats, or entire codebases.

• Grounded tool calls: Out‑of‑the‑box connectors for Google Search grounding, code execution, and URL context ingestion.

• 40 % cheaper audio input than the preview release, broadening use cases in multimodal pipelines. 

Speed and quality benchmarks

Google’s internal tests show Flash‑Lite beating both Gemini 2.0 Flash‑Lite and 2.0 Flash on median latency while posting higher accuracy across coding, math, science and multimodal tasks. That makes the model a strong candidate for user‑facing workloads where every millisecond counts but hallucination control still matters—think chat assistants, translation layers or real‑time content moderation. 

Early adopters prove the case

Several partners have already swapped in Flash‑Lite during preview:

• Satlyt cut satellite‑telemetry latency by 45 % and power draw by 30 %.

• HeyGen now translates avatar videos into 180+ languages on the fly.

• DocsHound crunches long demo footage into training docs “in minutes rather than hours.”

• Evertune scans massive corpora of model outputs for brand analysis at production speed. 

Getting started in minutes

Developers can invoke the new model simply by specifying gemini-2.5-flash-lite in the Gemini API, Google AI Studio, or Vertex AI. If you used the preview alias, switch to the GA name before Google retires the preview endpoint on August 25. 

Why this release matters

Flash‑Lite cements Google’s multi‑tier strategy: Pro for maximal reasoning, Flash for balanced workloads, and Flash‑Lite for blazing‑fast requests at commodity prices. With its million‑token window, built‑in tool calling, and turn‑key availability on Google Cloud, the model lowers the barrier for startups and enterprises to embed powerful generative AI into latency‑sensitive products—without blowing their budget.

For AI enthusiasts, Flash‑Lite is a reminder that the race isn’t just about bigger models—it’s about smarter engineering that delivers more capability per chip cycle and per dollar. Whether you’re building a real‑time translator, an automated doc parser, or a fleet of micro‑agents, Gemini 2.5 Flash‑Lite just became one of the most compelling tools in the open cloud arsenal.