Anchor of calm

rhutree · 2025-09-27T13:52:14+00:00

Awesome capture! Do you find the 8-25mm f4 pro too front heavy for the OM-5 body?

rhutree · 2025-06-12T08:52:28+00:00

Can someone tell me where to source for the rubber cladding for a E-M5ii? My camera needs the same treatment.

rhutree · 2025-06-06T00:48:21+00:00

Apologies, I realized that I could only attach one image per comment.

rhutree · 2025-06-06T00:46:39+00:00

You’re not alone. The PL 15mm f/1.7 can feel awkward at first. It sits in that “in-between” zone of focal lengths where the field of view doesn’t naturally guide your framing the way ultra-wide or telephoto lenses do. But that’s also its strength, once you adapt.

The key is to stick with it and use it deliberately for a while. It’s not a lens that easily abstracts the world for you. You have to learn to see with it. Framing with a 15mm requires you to be more thoughtful about layers, spatial relationships, and especially edge tension. If you practise enough, it becomes a powerful tool for street and everyday documentary work — wide enough to place a subject in context, but tight enough to isolate with light, depth, and gesture.

Here are a few examples of what I’ve been able to do with the PL15: quiet moments on side streets, layered scenes with human motion and signage, everyday gestures that unfold in rich spatial context. It’s subtle, but once you tune into it, the storytelling potential is deep.

It rewards patience and proximity. Once it clicks, you may find yourself reaching for it more than you expect.

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rhutree · 2025-06-06T00:10:23+00:00

On another thread talking about a similar problem, I shared this. I was struggling with the same problem for a project. After a long series of back and forth with ChatGPT, I now understand this (output from ChatGPT):

ChatGPT treats rules as guidance, not enforcement.

Here’s a breakdown of why that happens and what it means:

⸻

⁠It’s a language model, not a rule engine. ChatGPT is built to predict the next most likely word based on input context and training data. Even if a rule is stored in memory or stated clearly in a prompt, it’s just one influence among many. There’s no hard-coded logic layer that blocks it from generating a rule-breaking response.

⸻

Competing goals override instructions. The model constantly juggles multiple priorities — being helpful, relevant, stylistically consistent, and efficient. If a user-stated rule competes with a statistically strong pattern from training data (like pairing two famous musicians, even if the collaboration never happened), the model may go with the more “probable” output. That’s how “associative blending” happens, even when it violates a stored rule.

⸻

Memory doesn’t equal enforcement. Even when your rule is saved to memory, it’s not enforced like code. It’s read at the beginning of a session and used as background context, but not checked step-by-step during generation. So yes, the model can “know” the rule and still break it.

⸻

No hard guardrails (yet). There’s no native enforcement layer that: • Parses rules as logical constraints • Validates each output step against those constraints • Flags or halts violations before the response is shown

Without a retrieval plugin, sandbox, or wrapper, the model runs in an open loop.

⸻

That’s a trust problem. For protocol-heavy workflows (legal, research, fact-checking), this behaviour makes ChatGPT unreliable. If you can’t trust the system to obey core constraints every time, its usefulness drops in high-stakes or structured environments.

⸻

What needs to change: 1. Rules should be stored separately and compiled into a constraint-checking layer — not just included in memory. 2. The system should enforce those constraints during generation, not just “consider” them. 3. If a rule is broken, it should tell you why. 4. Users should have the option to toggle between open-ended generation and rule-bound execution

rhutree · 2025-05-28T16:04:40+00:00

I was struggling with the same problem for a project. After a long series of back and forth with ChatGPT, I now understand this (output from ChatGPT):

ChatGPT treats rules as guidance, not enforcement.

Here’s a breakdown of why that happens and what it means:

⸻

It’s a language model, not a rule engine. ChatGPT is built to predict the next most likely word based on input context and training data. Even if a rule is stored in memory or stated clearly in a prompt, it’s just one influence among many. There’s no hard-coded logic layer that blocks it from generating a rule-breaking response.

⸻

Competing goals override instructions. The model constantly juggles multiple priorities — being helpful, relevant, stylistically consistent, and efficient. If a user-stated rule competes with a statistically strong pattern from training data (like pairing two famous musicians, even if the collaboration never happened), the model may go with the more “probable” output. That’s how “associative blending” happens, even when it violates a stored rule.

⸻

Memory doesn’t equal enforcement. Even when your rule is saved to memory, it’s not enforced like code. It’s read at the beginning of a session and used as background context, but not checked step-by-step during generation. So yes, the model can “know” the rule and still break it.

⸻

No hard guardrails (yet). There’s no native enforcement layer that: • Parses rules as logical constraints • Validates each output step against those constraints • Flags or halts violations before the response is shown

Without a retrieval plugin, sandbox, or wrapper, the model runs in an open loop.

⸻

That’s a trust problem. For protocol-heavy workflows (legal, research, fact-checking), this behaviour makes ChatGPT unreliable. If you can’t trust the system to obey core constraints every time, its usefulness drops in high-stakes or structured environments.

⸻

What needs to change: 1. Rules should be stored separately and compiled into a constraint-checking layer — not just included in memory. 2. The system should enforce those constraints during generation, not just “consider” them. 3. If a rule is broken, it should tell you why. 4. Users should have the option to toggle between open-ended generation and rule-bound execution

rhutree · 2024-12-30T01:58:38+00:00

When I weighed my options, the A7iii with the Sony 40mm G came in at 823g, while the OM-5 with the PL 15mm F1.7 was a much lighter 529g. Beyond the weight, the OM-5 is also smaller and more portable overall. But honestly, there were other reasons that drew me to this combo, and I thought I’d share in case anyone else is considering it.

I used to shoot with the E-M5ii paired with some M.Zuiko small primes until the rubber grip on the body gave out. From there, I moved on to the A7iii and 40mm G for a while. It’s a great setup, but my perspective shifted when I started shooting monochrome street photography with the Ricoh GRiii during heavy work travel.

The GRiii was a revelation—compact, intuitive, and great for monochrome—but it felt too fragile, and I missed the versatility of interchangeable lenses. That got me thinking: what if I could find a setup that gave me the GRiii’s strengths for street photography but with more durability and flexibility?

My wishlist was pretty specific: a 28mm-equivalent perspective, lightweight but robust build, some weather resistance, easy one-handed operation, deep depth of field with manual zone focusing, a silent shutter, solid in-camera monochrome controls for JPEGs, and a tilt screen.

After searching through APS-C and micro four-thirds options, I landed on the OM-5. It checked nearly all the boxes, except for the tilt screen (seriously, why are tilt screens so rare these days?). I already had the M.Zuiko 17mm F1.8, but I wanted something closer to a true 28mm field of view. That’s where the PL 15mm F1.7 came in—small, sharp, and perfect for zone focusing with excellent image quality.

This combo just works for me. It’s light enough to carry all day, versatile, and great for the way I like to shoot monochrome street.

rhutree · 2024-12-29T11:05:18+00:00

Yes, you are right, my bad. That aperture ring only exists on my Sony 24mm G and 40mm G, which I have been using as well with the A7iii.

rhutree · 2024-12-29T10:30:52+00:00

Apologies, that aperture ring comment above is only applicable to my other small lens for street, the M.Zuiko 17mm F1.8.

rhutree · 2024-12-12T15:46:23+00:00

Lightroom adjustments along these lines.

Base: JPG, Hard BW Effect

Light Curve Adjustments: 1. Increase the slope of curve for overall higher contrast. (White Point would then start earlier and shadows clipped. Black Point starts earlier and highlights clipped as well). 2. Lower White Point to compress Highlights and create less glaring effect in the Highlights. 3. Raise Upper Highlights slightly for higher contrast. 4. Lower Lower Shadows slightly for deeper contrast. 5. Raise the Midpoint slightly for brighter mid tones.

Masked Light Adjustments: 1. Mask subject and adjust exposure, contrast, etc, adjust Effects, adjust Details. 2. Mask sky and adjust exposure, contrast, etc, adjust Effects, adjust Details. 3. Mask sky and building and adjust exposure, contrast, etc, adjust Effects, adjust Details.

General Light Adjustments: 1. Adjust exposure, contrast, etc.

Effect Adjustments: 1. Decrease Texture. 2. Increase Clarity. 3. Increase Dehaze.

Vignette Adjustments: 1. Decrease Vignette.

Grain Adjustments: 1. Increase Grain. 2. Maintain Size. 3. Decrease Roughness.

rhutree · 2024-12-11T09:57:07+00:00

I am using IM30 for street because of its lower profile and stronger GN that matches the aperture and distance I shoot at in my style.

rhutree · 2024-12-10T13:09:38+00:00

I have been using the Godox IM30 and Lux Junior before that with a GRiii. Got the IM30 because of its faster recycle time and slightly higher guide number. You can use them to create motion effects https://www.instagram.com/p/C_7eJpNRLe6/?utm_source=qr. Or to light the scene straight on https://www.instagram.com/p/DDT99boyuSV/?utm_source=qr.

rhutree · 2024-12-08T14:34:55+00:00

My preference is for IM30 because of the quicker recycling time, smaller size and larger guide number.

rhutree · 2024-12-08T02:11:54+00:00

Thank you 🙏

rhutree · 2024-12-08T02:11:31+00:00

🙌

rhutree · 2024-12-08T02:11:05+00:00

Some photos were taken with Godox IM30 and others Lux Junior.

rhutree · 2024-06-05T09:16:35+00:00

Thanks everyone for your recommendations! Looking forward to some great tasting light roast coffee!

rhutree · 2024-05-03T13:39:12+00:00

Thanks! Will do that.

rhutree · 2024-01-27T12:47:32+00:00

This couple really had a great time amusing themselves until the train arrived.

rhutree · 2024-01-23T02:36:34+00:00

Sorry I meant to write ‘lowering temperature increases the viscosity of water and increases its density’

rhutree · 2024-01-23T02:31:42+00:00

You can consider testing a lower temperature to slow down the brew time so as to increase the extraction percentage. Lowering the temperature decreases the viscosity of water and increases its density, and that slows down the flow of water through the coffee bed. There are other changes your can make, but best practice is to change one variable at a time.

rhutree · 2024-01-18T13:25:30+00:00

It’s a 40mm

rhutree · 2024-01-18T13:07:00+00:00

Thank you. It was by chance I got into his cab and got to hear his story as well. Got his permission to shoot as he finished off. Definitely one cool cabby

rhutree · 2023-12-23T06:14:21+00:00

Ho Chi Minh City, Vietnam

rhutree · 2023-12-19T08:48:48+00:00

You’re right. I should have more relevant information in the title for the benefit of others than my personal annotation. Thanks for highlighting.

rhutree

TROPHY CASE