GLM-5 First Look: What Changed for Personal AI

Hi, Anna here. This week, I hit that familiar, slightly embarrassing wall: my notes were sprawled across three apps, I had a half-finished meal plan in a text file, and a tiny project (reorganizing my bookshelf) kept sliding day to day. I wanted a nudge, not a system. So I opened GLM-5 with low expectations and a cup of tea.

This GLM-5 review is from a few quiet, real runs in early February 2026. No flashy demos, no dev setup. Just small, ordinary tasks where I noticed either less friction, or not. If you're curious whether GLM-5 actually helps with daily routines and small personal projects, here's what stood out to me, where it stumbled, and a few simple tests you can try yourself.

GLM-5 is live — what’s actually different

I'm not here to dissect model cards or parade benchmarks. I care about how a model feels in a normal Tuesday. That said, a couple shifts in GLM-5 were noticeable enough in practice to mention up front. (If you want the official perspective, the official GLM developer docs are the best canonical source.)

Reasoning and multi-step planning

In short: it plans a bit more cleanly, and it's less prone to jump straight to the "final answer" when the task needs intermediate steps. I noticed this while sketching a weekend plan with a few constraints (budget, time windows, one shared car). GLM-5 offered a draft plan, then, unprompted, listed trade-offs and a tiny "if/then" for what to cut if we ran late. That if/then bit is new to me in how casually it appeared. It wasn't flawless, but it reduced that micro-decision fatigue I usually feel.

It also handled backtracking better. When I changed one input (a friend couldn't make Saturday morning), it reflowed the plan without scrapping the good parts. Older models I use often toss everything and start fresh: GLM-5 seemed to keep more of the thread. Not perfect, but closer to how I think when I'm trying not to overcomplicate a simple day.

Context handling and long conversations

Longer chats felt slightly sturdier. I ran a 90‑minute on/off conversation that hopped between groceries, a bookshelf re-org, and drafting a short email. GLM-5 kept references alive more reliably than I expected, like remembering which shelf held travel books and not re-suggesting things I'd already ruled out. After a while, it still drifted, especially when I layered in new sub-goals quickly. But the "please don't make me repeat myself" moments were fewer.

I won't guess at exact context window numbers here: I don't have them. What mattered practically: I could nudge it, "we already decided on the salmon, remember?", and it corrected itself without getting defensive or inventing fake certainty. That small social grace saves energy.

Creative writing and tone

Tone felt easier to steer. I tried a warm-but-brief email to a neighbor about borrowing a drill, and a short caption for a craft post. GLM-5 took soft tonal cues better than I'm used to, "friendly but not chirpy," "helpful, two sentences max." It still over-embellished when I got vague ("make it fun"), but when I gave one or two concrete boundaries, it landed closer to my voice. The drafts were more like scaffolding I wanted to keep rather than rewrite from scratch.

3 quick tests anyone can run (no dev setup)

I like tests that take five minutes and don't require a new account, a terminal, or a week of configuration. These three gave me a good feel for GLM-5's strengths and edges.

Daily planning task

Prompt I used: "I have 2.5 hours Saturday afternoon. I need to 1) return a package, 2) prep three dinners under $40 total, 3) clear one shelf of books. I have a bike, no car. Please draft a sequence, travel times, a short grocery list, and one alternate plan if I'm running 20 minutes late."

What happened: It gave me a reasonable route order (store, drop-off, home), included time cushions, and a grocery list that mostly matched my staples. The quiet win was the fallback: "If you're 20 minutes late, skip marinating and batch-chop veggies: shift one dinner to a sheet-pan version." That's the kind of small constraint-aware thinking that keeps me moving.

Friction: It confidently listed one ingredient my local store rarely carries. When I said "assume a mid-size store with limited spices," it corrected cleanly. First pass: 6/10 helpful. After one follow-up: 8/10.

Summarize-and-act task

Prompt I used: I pasted a 900‑word mishmash from my notes: a recipe idea, two to‑dos from a call, and a half-drafted message to a friend. Then I asked: "Summarize each strand in 1–2 lines, pull out action items with deadlines, and draft a text I can actually send."

What happened: The summaries were crisp. Action items came back as a short checklist (with believable deadlines), and the draft text sounded like me after I nudged it: "less formal, keep the favor small." I sent the message with one edit.

Friction: On the first try it invented a deadline I hadn't written. When I said "no new dates," it fixed it. Worth noting: GLM-5 seems eager to be useful: if your instructions are loose, it may fill gaps. That can be good or mildly annoying depending on your tolerance.

Emotional context task

Prompt I used: "I'm avoiding a tiny task (updating address info) for dumb reasons. Please give me a 10‑minute plan that feels kind, not scolding. Two options: one if I have energy, one if I'm fried."

What happened: It offered two variants that didn't sound like a productivity coach. The ‘fried' version had a timer, one micro-step, and a permission to stop after updating just billing. That tone landed well after a long day.

Friction: Occasionally it slipped into pep-talk clichés. When I said "skip motivational language," it complied and the plan felt calmer. Small but meaningful difference.

What Macaron users notice first

If you live in a lightweight routines-and-notes setup, quick checklists, small recurring nudges, nothing enterprise, GLM-5 slots in quietly. What I noticed (tested Feb 2026):

• It's good at friction-aware micro-plans. If your Macaron boards (or whatever simple tool you use) hold small, repeating tasks, meal prep, practice sessions, tidying sprints, GLM-5 takes those crumbs and proposes tiny, respectful sequences without insisting on a new "system."
• It remembers soft preferences within a session. If you nudge it once, "no elaborate recipes on weekdays," "max 30 minutes for practice", it tends to honor that tone in later suggestions. It's not a long-term memory vault, but in-session recall felt steadier.
• Drafts that feel 70% done. For short notes, captions, or check-in messages you'd normally bang out and tweak, GLM-5's first drafts needed fewer heavy edits. I still changed phrasing, but the structure and length were right more often.

What you won't get out of the box: I didn't test any deep integration or automations. If your Macaron flow depends on API triggers or background agents, you'll need to look up current capabilities. My runs were just: copy notes in, get a plan or a draft out. Simple on purpose.

Honest limits — what’s still rough

A few edges showed up repeatedly across a handful of sessions.

• Over-helpfulness. GLM-5 sometimes invents a deadline or a step to be "useful." If you like that initiative, great. If not, add guardrails: "no new dates," "don't assume I have ingredients beyond this list," "keep plan under 5 steps." It responds well to boundaries.
• Long-thread drift. In sprawling chats with fast context switches, it still loses minor details. A quick nudge usually corrects it ("we dropped that option"), but don't expect perfect recall after ten topic jumps.
• Tone creep. If you ask for "encouraging," it can edge into poster-slogan territory. Saying "plain tone, no pep talks" fixed it for me.
• Facts on the edges. For niche store inventories, hyper-local travel times, or specific product availability, it guesses. That's normal for a general model, but it's worth calling out. When it matters, verify.
• Latency blips. Most replies were snappy: a few took longer than I expected for modest prompts. Nothing wild, just enough to notice when I was mid-task.

I didn't test tool calling, image inputs, or any advanced workflows here. If those are crucial to you, check current documentation or try a focused sandbox before you switch. Keeping scope honest makes the results more trustworthy.

To be honest, what truly makes the fragmented daily routine smooth is not a complex system, but something that can directly capture, organize, and transform small tasks, notes, and reminders into actionable items. Our Macaron can help you solve this problem.

Click here to give it a try!

Should you switch now? (decision tree)

Here's the low-drama version I used for myself. If you want something fancier, you probably don't need an article, you need a weekend and coffee.

Do you mostly want calmer daily planning and kinder micro-prompts?

• Yes: Try GLM-5 for a week. Use it for one recurring routine and one tiny project. Keep prompts specific about limits.
• No: Stick with what you have: this isn't a silver bullet.

Do you rely on the model to remember soft preferences inside a single afternoon session?

• Yes: GLM-5 felt steadier there than many peers I've used recently. Worth a trial.
• No: Any solid general model will do: switching won't change much.

Are you sensitive to tone? (You want helpful, not hype-y.)

• Yes: GLM-5 takes tone constraints well if you spell them out. Add "no pep talk," "two sentences," or "neutral, friendly."
• No: You'll be fine anywhere.

Do you need rock-solid long-thread memory across days?

• Yes: I wouldn't switch for that alone. You'll still need to re-anchor or summarize.
• No: You're in the model's sweet spot, short to medium tasks.

Do you want integrations or automations today?

• Yes: Check the current ecosystem and the official GLM developer docs. My review didn't cover this, so treat it as an unknown.
• No: GLM-5 is an easy drop‑in for copy‑paste workflows.